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GeoPlanning 
Journal of Geomatics and Planning             Geoplanning: Journal of Geomatics and Planning, Vol. 12, No. 1, 2025, 95 - 122 

   

Original Research 

Tourism Potential Zone Mapping for The State 

of Madhya Pradesh, India Using MCDM and 

Machine Learning Models 

Shrinwantu Raha1*, Sayan Deb1 

1. Department of Geography, Bhairab Ganguly College, Belgharia, Kolkata, India  

 

 

DOI: 10.14710/geoplanning.12.1.95-122 

Abstract  

The rich and diverse tourism attractions of Madhya Pradesh have long been recognized, but the Tourism Potential Zones 

(TPZs) have yet to be clearly identified. This research aimed to uncover these hidden potentials using a combination of 

Multi-Criteria Decision Making (MCDM) and machine learning techniques. TPZ was predicted using a approaches, 

including Analytic Hierarchy Process (AHP), Linear Model (LM), Elastic Net Model (EN), and K-Nearest Neighbors 

(KNN). Further, by combining the above models, a new ensemble model (AHP-LN-EN-KNN ensemble) was prepared. We 

followed the ROC-AUC (Area Under Curve) and Root Mean Squared Error (RMSE) as evaluation measures. The findings 

reveal a landscape of promise, with each model with accuracy levels ranging from 81.4% to 90.6%. The AUC values for the 

models ranged from approximately 70% to 95%, while the RMSE values ranged from 0.8 to 1.3. The ensemble model 

appeared with better accuracy (for training set 0.92 and for test set 0.88), higher AUC value (for training set 94.5% and for 

test set 89.4%) and the lowest RMSE (i.e., 0.71) value. On the other hand, the AHP was identified with higher combined 

RMSE (i.e., combined RMSE 1.08) and diminished AUC (i.e., for training set 70.1% and test set 70.2%). The northern, 

south-western, and middle regions emerge as high-potential areas, whilst the south-western edges languish with less 

promise. Meanwhile, the north-western expanse offers a scene of moderate potential. These findings not only inform, 

inspire, laying a foundation for Madhya Pradesh's long-term tourist growth.  

Copyright © 2025  by Authors,  

Published by Universitas Diponegoro Publishing Group.  

This open access article is distributed under a  

Creative Commons Attribution 4.0 International license 

 

1. Introduction 

Tourism, which is hard to define, may promote a region's environmental, social, and economic growth 

(Telfer & Sharpley, 2015). However, its’ enigmatic character requires practicality, precision, and efficacy to fulfill 

its’ revolutionary potential (Harianto et al., 2020). Tourism's dynamic and unpredictable tapestry is hidden 

between exquisite delicacy and cultural sanctity (Smith, 2015). (Atun et al, 2019) describe tourism potential as a 

complex web of social, cultural, economic, and infrastructural factors. This initiative attracts tourists with its’ 

appealing melody of accessibility and lofty quest of guardianship over multiple valuable resources, producing a 

tapestry of attraction that captivates the adventurous soul (Saner et al., 2019). Underutilization persists across 

sectors and paradigms, awaiting its’ transformational potential (Ramírez-Guerrero et al., 2021). Tourism 

potentiality goes beyond asset accumulation to turn sites into wanderlust hangouts (Raha & Gayen, 2022a). It 

nurtures a tourist-environment relationship by curating and protecting a destination's soul (Sarker, 2018). The 

evaluation of tourist potential embodies complex calculation that defines a full mosaic of criteria within the 

authority of the United Nations World Tourism Organisation (UNWTO) (Trukhachev, 2015). Within this 

e-ISSN: 2355-6544 
 
Received:  09 March 2024;  
Revised:  12 May 2025;  
Accepted:  18 May 2025;  
Available Online: 18 May 2025; 
Published:  26 May 2025.  
 
Keywords:  
Tourism Potential Zone (TPZ), 
K-Nearest Neighbors Model, 
Analytic Hierarchy Process 
 
*Corresponding author(s) 
email: raha@gmail.com  
 

 

https://doi.org/10.14710/geoplanning.12.1.95-122
mailto:raha@gmail.com


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96 

framework, geology, relief, aspect, and water proximity influence landscape appeal all interact to shape the 

landscape's attraction (Yamin et al., 2021). The complex accessibility network, depicted by road and railway 

density measures vs distance metrics, controls accessibility dynamics. Demographic variables including sex ratio, 

literacy, population, and growth rates suggest societal vitality, whereas tourism density enhances the region's 

appeal (Stępniak et al., 2019). A complex tapestry of these factors defines visitor potentiality, symbolizing the 

UNWTO's holistic approach to TPZ evaluation. In fact, exploring the potential of tourism becomes an appeal 

for countries to develop in unison, strengthening the fabric of culture and way of life. It is an enlightening 

journey, a pilgrimage to protect the integrity of our nature and our shared past. 

The Indian Planning Commission considers tourism the nation's second-largest business since it creates 

low- and intermediate-skill jobs (Rajan, 2018). India's business lacks infrastructure and coordination despite its’ 

historical and natural charms (Mamun & Mitra, 2012). In Madhya Pradesh, TPZs are poorly defined (Aijaz, 

2022). Due to efficiency and simplicity, GIS and multi-criteria decision-making approaches, notably the Analytic 

Hierarchy Process (AHP), are increasingly employed for this purpose (Raha & Gayen, 2022a, 2022b). Statistical 

and machine learning models are also emerging alongside MCDM models. The Linear model (LM) is a statistical 

regression approach that performs well with GIS. The supervised learning algorithms along with the LM model 

are widely used in the landslide prediction (Dong et al., 2011), ground water potentiality assessment (Naghibi et 

al., 2015), eco-tourism potential assessment (Mirsanjari & Mirsanjari, 2012; Zhang et al., 2024), forest fire site 

detection (Tien Bui et al., 2016), and other fields. The Elastic Net (EN) model reduces overfitting and 

multicollinearity. EN model has been also widely used in the hazard mitigation (Suchting et al., 2019), landslide 

prediction (Zhu et al., 2023), flood prediction (Al-Areeq et al., 2023), non-linear tourist behaviour prediction 

(Brida, 2018), picture categorization (Soomro et al., 2016), and more.  

The KNN algorithm swiftly categorizes fresh data (Okfalisa et al., 2017). K-Nearest Neighbour (KNN) 

has three advantages: 1) it is fast to compute, 2) it predicts better than other models, and 3) its’ output is simple 

to read (Okfalisa et al., 2017). After storing all data, the KNN model classifies fresh data points using distance 

functions. The KNN model is widely used for the analysis of credit rankings, (Chen et al., 2011) pattern 

recognition (Mir & Nasiri, 2018), data mining (Mohanapriya & Lekha, 2018), intrusion detection (Aburomman 

& Ibne Reaz, 2016), face recognition (Nugrahaeni & Mutijarsa, 2016; Sugiharti et al., 2020), and for the analysis 

of health related data (Mittal et al., 2019). Varied models have varied accuracy levels, hence integrated ensemble 

models are needed to explain real-world events. 

Out of these methods, the AHP have been applied widely for the TPZ identification (e.g., Raha et al., 2024, 

Sahani, 2019). However, the EN, KNN and LM methods have been applied rarely for the prediction of TPZs. 

Moreover, their performance has not also been compared in literature. A new ensemble model was also prepared 

in this research by taking average of AHP, LM, EN, and KNN models. Further, till now, the tourism potentiality 

of the state of Madhya Pradesh is unexplored. Therefore, the objectives of the present research are as follows: 

To evaluate and compare the performances of one MCDM technique (i.e., AHP technique), 3 machine learning 

models (i.e., LM, KNN and EN model) in the prediction of TPZ, and to prepare one ensemble model to improve 

the performances of the input algorithms (i.e., AHP, LM, EN and KNN models) for the prediction of TPZ for 

the state of Madhya Pradesh. The TPZs were explored through an integrated 9- step process in this research. 

The Receiver Operating Characteristic (ROC) curve and Root Mean Squared Error (RMSE) have been adopted 

to evaluate and compare the machine learning and MCDM models. This research beautifully utilized the GIS 

platform to spatially illustrate the input data as well as the TPZ. 

2. Data and Methods 

2.1. Study Area  

A rich cultural and historical history exists in Madhya Pradesh. Tourism studies favor Madhya Pradesh, 

the "Heart of India," in the midst of the Indian subcontinent for its historical, cultural, and natural splendor. 

Bhopal, Indore, Gwalior, Ujjain, and Jabalpur are major cities in Madhya Pradesh. North, southeast, south, west, 

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97 

and north-west boundaries of Madhya Pradesh are Uttar Pradesh, Chhattisgarh, Maharashtra, Gujrat, and 

Rajasthan Figure 1.  

 
     Source: Authors, 2023 

Figure 1. Location Map 

Madhya Pradesh has UNESCO World Heritage Sites including Sanchi Stupa and Khajuraho. For 

historians, archaeologists, and cultural anthropologists, the area is an engaging case study because of these 

archaeological wonders, which offer insightful information on the historical importance and architectural history 

of the region. Madhya Pradesh is a dynamic laboratory for comprehending the dynamic changes in 

infrastructure, accommodations, transportation, and tourism legislation  . The state comprises a unique scenario 

for its’ ecological and wildlife tourism through its’ wealth of natural beauty and biodiversity. Kanha National 

Park and Bandhavgarh National Park are just two of the many national parks and animal sanctuaries that 

Madhya Pradesh is home to. The existence of tigers and leopards, two iconic megafaunas, has drawn a lot of 

attention from the fields of wildlife conservation and tourist management. The region is also very rich with the 

tribal traditions and indigenous populations. Many different tribal communities (e.g., Bhil, Sahariya, Kharwar, 

Munda,Kol, Sora, Baiga, Kolba, Andh etc.), with unique customs (e.g. Ghotul), traditions, and artistic expressions 

(i.e., Mandana art etc), are found in Madhya Pradesh.  

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98 

There are many studies on tourism in Madhya Pradesh, including resource development (Pandey et al., 

2014), social media's impact on tourism (Gohil, 2015), art and craft tourism (Kumar et al., 2023), and tourism's 

economic effects (Sharma, 2019), role of mass tourism (Chandravanshi & Jain, 2023; Gohil, 2015) development 

of sustainable tourism sector (Kishnani, 2022), eco-tourism (Ahmad & Pandey, 2016) for the state of Madhya 

Pradesh, but hardly any research work is available on tourism potential zone identification on this tract. 

Therefore, the tourism potential zone identification for the state of Madhya Pradesh is a noble attempt. 

2.2. Methodology 

The methodological framework was marked in the Figure 2. The 9-step methodology was used in this 

research to demarcate the TPZ of the Madhya Pradesh state. 

 
Source: Authors, 2023 

Figure 2. The Methodology  

2.2.1. Data Acquisition 

The first step was to acquire all the data to be used in this research. All of the secondary data sources were 

illustrated in the Table 1. Here, the Geology (GL), Relief (RL), Aspect (AS), Distance from River (DR), Road 

and Railway Density (RRD), Distance from Road and Railway (DRRD), Sex Ratio (SR), Literacy Rate (LR), 

Total Population (TP) and Growth Rate (GR) were used. The Geological data was downloaded from the USGS. 

This is a compiled data, which includes petroleum geology, geological provinces, oil and gas fields of the South-

Asia. The data was available in a shapefile (.shp format) format, a part of the U.S. Geological Survey World 

Energy Project. For the efficient management and analysis of enormous amount of data; the World was classified 

into 8 energy regions and those are further subdivided into geologic provinces, on the basis of the natural 

geologic entities. These may often include a dominant structural element or a number of contiguous elements.  

UNESCO World geologic maps and other tectonic geologic maps helped to delineate the boundaries of 

major geologic provinces. Those shapefiles were amalgamated by the USGS from the UNESCO. The geologic 

maps of South and East Asia of 1976 and 1990 having scales of 1:10,000,000 and 1:5,000,000 were used here. 

The relief and Aspect maps were prepared from the Digital Elevation Model (DEM), synthesized by the Shuttle 

Radar Topography Mission (SRTM) having the spatial resolution of 30m. SRTM DEM was downloaded from 

USGS Earth Explorer. River, road and railway shapefiles (prepared from the maps of India online portal) were 

required for the preparation of DRRD and DR layer. Euclidian distance method Equation.1 was utilized to 

estimate the DRRD and DR spatial layer: 

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99 

𝑑 = √(𝑥2 − 𝑥1)2 + (𝑦2 − 𝑦1)2……………. (Eq.1) 

where, d - Euclidian distance, (𝑥2, 𝑥1) - is the point exist on the river or road, (𝑦2, 𝑦1) - is the closest point of the 

previous. 

The Euclidian Distance tool was used to estimate the distance from each cell in the raster to the closest 

source. The SR, LR, TP and GR were collected from the Census data prepared by Directorate of Census 

Operations Madhya Pradesh, Ministry of Home Affairs, Govt. of India (2011). The tourist spots were marked 

with the help of Google Earth and non-participant observation technique (Banik & Mukhopadhyay, 2022) 

(Figure 21). All of the secondary data sources used here contain an open-access licence agreement; which specify 

that any data can be used for the academic purposes by citing their original sources (Ruda, 2016). 

Table 1. Data Sources for Analyse the Tourism Potentiality 

 

2.2.2. Checking of Prerequisites 

a. Checking of Multicollinearity 

The correlation matrix was prepared to check whether any multicollinearity exist within the acquired 

data. For the efficient processing of machine learning and MCDM models, the basic prerequisite is the 

independent nature of the variable (Vairetti et al., 2024). If the correlation coefficient value comes >0.8 then the 

Sl 
no

. 
Criteria Sources of data Nature of data 

Relationship 
with tourism 

References 

1 Geology (GL) https://pubs.er.usgs.gov/publication/ofr
97470C 

Open access 
(Input/Independe
nt variable) 

 Rutherford et 
al. (2015) 

2 Aspect (AS) https://earthexplorer.usgs.gov/ (2014-
09-23) Resolution (30 meters * 30 
meters) 

Open access 
(Input/Independe
nt variable) 

Inverse Woźniak et al. 
(2018) 

3 Relief (RL) https://earthexplorer.usgs.gov/ (2014-
09-23) 
Resolution (30 meters * 30 meters) 

Open access 
(Input/Independe
nt variable) 

Inverse Sahabi Abed 
and Matzarakis 
(2018) 

4 Distance from 
River (DR) 

https://www.researchgate.net/post/Ho
w-to-get-a-River-basin-shape-files-of-
India-kindly-suggest-
any/615150d9584a141e805d5a83/citati
on/download 

Open access 
(Input/Independe
nt variable) 

Inverse Woźniak et al. 
(2018) 

5 Road & Railway 
Density (Road 
and Railway 
Shapefile) (RRD) 

https://grpbhopal.mppolice.gov.in/railw
ay-map &  
https://www.mapsofindia.com/maps/m
adhyapradesh/madhyapradeshroads.htm 

Open access 
(Input/Independe
nt variable) 

Proportional Wang et al. 
(2018) 

6 Distance from 
Road & Railway 
(DRRD) 
 

https://grpbhopal.mppolice.gov.in/railw
ay-map &  
https://www.mapsofindia.com/maps/m
adhyapradesh/madhyapradeshroads.htm  
Road and Railway Shapefile 

Open access 
(Input/Independe
nt variable) 

Inverse Raha et al. 
(2022b) 

7 Sex Ratio (SR) 

Directorate of Census Operations 
Madhya Pradesh 
Ministry of Home Affairs, Govt. of India 
(2011) 

 
 
 
 
 

Open access 
(Input/Independe

nt variable) 

Proportional  

8 Literacy Rate 
(2011) (%) (LR) 

Proportional 
Natalia et al. 
(2019) 

9 Total Population 
(2011) (TP) 

Proportional 
Trukhachev 
(2015) 

1
0 

Growth Rate 
(2011) (%) (GR) Proportional 

Banerjee (2014) 

1
1 

Density of 
Tourist Spots 
(TS) 

Non-Participant observation technique, 
Google Earth, Govt. Reports and 
relevant websites 

Dependent 
Variable Proportional 

Raha et al. 
(2022a) 

https://doi.org/10.14710/geoplanning.12.1.95-122
https://pubs.er.usgs.gov/publication/ofr97470C
https://pubs.er.usgs.gov/publication/ofr97470C
https://earthexplorer.usgs.gov/
https://earthexplorer.usgs.gov/
https://grpbhopal.mppolice.gov.in/railway-map
https://grpbhopal.mppolice.gov.in/railway-map
https://www.mapsofindia.com/maps/madhyapradesh/madhyapradeshroads.htm
https://www.mapsofindia.com/maps/madhyapradesh/madhyapradeshroads.htm
https://grpbhopal.mppolice.gov.in/railway-map
https://grpbhopal.mppolice.gov.in/railway-map
https://www.mapsofindia.com/maps/madhyapradesh/madhyapradeshroads.htm
https://www.mapsofindia.com/maps/madhyapradesh/madhyapradeshroads.htm


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100 

variables are considered as the potentially correlated and dependent. If it is less than 0.8 the variables are 

considered as the independent and then the models could be applicable on the collected data sets. 

b. Checking of Residuals vs. Fitted Plot, Normal Q-Q Plot, and Residuals vs. Leverage Plot 

Before applying the LM model, the residuals vs. fitted plot, normal Q-Q plots, scale location plot and 

residuals vs. leverage plot were checked. Residuals vs. fitted plot help to detect the non-linear nature, variances 

(equal/unequal) of error, potential biases and possible outliers (Chavan & Momin, 2017). Normal Q-Q plot help 

display the theoretical distribution of associated data sets (i.e., whether normal, exponential or gaussian 

distribution etc.) determining if two data sets originate from populations with a similar (run-of-the-mill) 

distribution is another benefit of it. There may be cases, where the results are affected due to the extreme data 

points, which are influential. The Cook’s distance was applied in the residuals vs. leverage graph to calculate the 

measure the reasonable range beyond which the data points might be influential (Zhao et al., 2020). 

2.2.3. Data Normalization 

The normalization of data is required to remove the redundancy of data, minimize the modification 

errors, and simplify the query process (Ayesha et al., 2020) of the data. The normalization of the data was done 

in the third step of the research. 

2.2.4. Data Partitioning 

In the fourth step, total 471 data points were extracted from the each of the raster layers. For the efficient 

analysis, those were subdivided into the training and test set. 70% of the total data points (i.e., 330 data points) 

were included in the training set and the 30% of the total data points were included in the test set (i.e., 141 data 

points) Figure 3.  

 
Source: Authors, 2023 

Figure 3.  Location of Training and Test points 

2.2.5. Setting the Input and Target Variables 

The tourist spots were added in the GIS platform and using the Inverse Distance Weightage (IDW) 

tool, the density of tourist spots (TS) layer was created. This raster layer was considered as the target 

(dependent) variable which was assumed to be influenced by the other independent variables in this research. 

Therefore, apart from the TS raster variable, other variables were considered as input variables. 

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101 

2.2.6. Employ the Analytic Hierarchy Process (AHP), Linear Model (LM), Elastic Net (EN) 

model and K-Nearest Neighbors (KNN) Model 

a. The AHP Model 

The AHP is a widely recognized multicriteria decision making tool (Saaty, 1980), which was used in this 

research to put the weightage of each thematic layer. AHP is an objective mathematical procedure that allows 

the incorporation of subjective and objective choices. AHP's broad application is a result of its ease of use, 

readiness, and high degree of flexibility (Sahani, 2019). In decision making, the AHP approach incorporates 

a hierarchical structure of different criteria in a pairwise comparison method (Saaty, 1980). AHP matrices display 

the uniform number of rows and columns (Raha & Gayen, 2022a). Each criterion is rated against the other 

criteria by assigning a relative priority scale of 1 (Equal importance), 3 (Moderate importance), 5 (very strong) 

and 9 (Extreme importance) to construct a pair-wise comparison matrix. 2,4,6 and 8 were the intermediate 

values. Relative priority scale was assigned to each of the spatial layer and their classes using the 

recommendation of 5 expert panel. Each expert had more than 5 years of experience in the field of travel and 

tourism.  

The experts signed a separate informed permission letter confirming that their replies would be utilised 

solely for academic reasons without revealing their identities. For example, in the Table 2, SR and GR are less 

important than GL and RL. Therefore, SR and GR were coded as 2 and the GL and RL were coded as 9 and 8 

respectively. Similarly, RRD is less important than GL. Therefore, the RRD was coded as 3 and the GL was 

coded as 9. Here, GL was identified with the highest priority (23.8% weightage) followed by the RL, AS (18.4% 

weightage), DR (11% weightage), DRRD (8.2% weightage), RRD (5.7% weightage), TP (4% weightage), LR (3% 

weightage), SR (2.5% weightage), and GR (1.8% weightage). All subclasses of each thematic layer was rated 

based on the causative factors on which the tourism phenomena triggers. Here, higher rating indicates higher 

tourism potential value. The weightage was estimated by the AHP using the following formula Equation. 2 

0 < 𝑤 < 1; ∑ 𝑤𝑖𝑗
𝑛
𝑖=1 = 1……………. (Equation 2) 

where, Consistency Ratios (CRs) were calculated to determine, whether pairwise comparisons were consistent 

or inconsistent. It was calculated as follows Equation. 3 : 

𝐶𝑅. =
𝐶.𝐼.

𝑅.𝐼
……………. (Equation 3) 

where, 𝐶. 𝐼. =
𝜆𝑚𝑎𝑥−𝑛

𝑛−1
……………. (Equation 4) 

where, C.I. is the Consistency Index; R.I. is the Random Index. If the CR is <0.1 the index was considered as the 

consistent (Saaty, 1980). In this research, consistency ratios of all matrices Table 4 were <0.1 so, all of the 

matrices were appeared as consistent. 

b. Linear Model (LM) 

The linear model (LM) is expressed here as follows Equation. 5: 

𝑦 = 𝑎0 + 𝑎1𝑥1 + 𝑎2𝑥2 + 𝑎3𝑥3 + ⋯ … … … … . . +𝑎𝑛𝑥𝑛……………. (Equation.5) 

where, y is the target variable; which is the density of tourist spot raster. 𝑥1, 𝑥2, 𝑥3 … … . . 𝑥𝑛 are the variables in 

the linear model specified in the Table 1. 𝑎1, 𝑎2, 𝑎3 … … . . 𝑎𝑛 are the coefficients. 𝑎0 is the parameter of the model. 

The parameters a and b are estimated using the ordinary least square (OLS) procedure. The OLS method is 

implemented by minimizing the actual and predicted value.  

c. Elastic Net Model (EN) 

The EN model is an enhanced iteration of the machine learning-based regression model that incorporates 

both lasso and ridge regression. Equation. 6, Equation. 7, Equation. 8: Modifying the Equation. 5, we can write 

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102 

𝑆𝑆𝐸𝑅𝑖𝑑𝑔𝑒 = ∑ (𝑥𝑖 − �̅�𝑖)2𝑛
𝑖=1 + 𝜆(𝑎1

2 + 𝑎2
2 + 𝑎3

2 + ⋯ … … . +𝑎𝑛
2) ……………. (Equation 6) 

𝑆𝑆𝐸𝑙𝑎𝑠𝑠𝑜 = ∑ (𝑥𝑖 − �̅�𝑖)2𝑛
𝑖=1 + 𝜆(⌈𝑎1⌉ + ⌈𝑎2⌉ + ⌈𝑎3⌉ + ⋯ … … . +⌈𝑎𝑛⌉) ……………. (Equation 7) 

𝑆𝑆𝐸𝐸𝑁 = ∑ (𝑥𝑖 − �̅�𝑖)2𝑛
𝑖=1 + 𝜆[(1 − 𝛼) ∑ 𝑎2 + 𝛼|𝑎|𝑛

𝑖=1 ] ……………. (Equation 8) 

where, Equation. 6, Equation 7, and Equation 8 are known as the ridge, lasso and Elastic Net regression model 

(EN) respectively. 𝑥𝑖   is the observed model variable; �̅�𝑖   is the predicted model; SSE is the sum of the squared 

error. 𝜆 is the regularization parameter, that controls the amount of regularization applied. By adding the 

regularization term, the magnitude of the regression coefficients (a) are penalized by the ridge, lasso and EN 

models. If the α value tends to 0 the Equation 8 is transformed into Equation 6 ; and when the α value tends to 

1 then Equation 8 is transformed to Equation 7. 

For the effective use of the EN model; the fraction deviance plot and log lambda plot were used. The 

fraction deviance plot illustrates that how the model coefficients Varies with the increase or decrease of the 

fraction deviance. It helps to determine the larger or smaller coefficients with the changing nature of fraction 

deviance value. The log lambda plot is essential to know the coefficient scores as a function of log (𝜆).  The top 

numbering of the plot indicates the number of predictors (variables) the model.  

d. K-Nearest Neighbors Algorithm (KNN) Model 

The K-nearest neighbors algorithm, sometimes referred to as KNN or k-NN, is a supervised learning 

classifier that employs proximity to produce classifications or predictions about the grouping of a single data 

point (Boateng et al., 2020). Although it may be applied to classification or regression issues, it is commonly 

employed as a classification method since it relies on the idea that comparable points can be discovered close to 

one another (Zhang et al., 2017). It generalizes well to multi-class problems and can learn complex decision 

boundaries when combined with ample data. Additionally, as it does no training step beyond caching the dataset, 

it's very effective in situations where training speed is essential and memory resources are ample. Here, the 

Euclidian distance metric was used to determine the distance between the given point and query point. The 

partitioning of various category datasets is one of the decision boundaries that this KNN model aids in 

determining (Bansal et al., 2019). 

2.2.7. Ensemble Model (AHP-LM-EN-KNN Model) 

The ensemble models were prepared by taking average of AHP, LM, EN, and KNN models (Huang et al., 

2024). These models were prepared using following equations Equation 9: 

𝐸𝑛𝑠𝑒𝑚𝑏𝑙𝑒_𝑚𝑜𝑑𝑒𝑙 =
𝐴𝐻𝑃 𝑚𝑜𝑑𝑒𝑙+𝐿𝑀 𝑚𝑜𝑑𝑒𝑙+𝐸𝑁 𝑚𝑜𝑑𝑒𝑙+𝐾𝑁𝑁 𝑚𝑜𝑑𝑒𝑙

4
……………. (Equation 9) 

Before proceeding with the ensemble model preparation, we evaluated the inter-model correlation coefficient. If 

the correlation coefficient exceeds 0.8, suggesting a high level of correlation between the models, ensemble 

approaches may not be appropriate (Cankurt & Subasi, 2022). 

2.2.8. Weighted Sum 

The Variable Importance Plot (VIP) was used to determine the weightage of each criterion utilized in 

AHP, LM, EN and KNN models. VIP is a popular global method to rate the importance of criteria involved in a 

model by rating the criterion 0 to 100 based on the priorities in the model. 0 means the lowest priority and 100 

indicates the highest priority. Those weightages are used to determine the weighted sum model; which is marked 

as the TPZ Equation 10. (Mitra et al., 2022): 

𝑇𝑃𝑍 = ∑ 𝑤𝑖
𝑇𝑃𝑍𝑛

𝑖=1 ……………. (Equation 10) 

where, TPZ is the Tourism Potential Zone; 𝑤𝑖
𝑇𝑃𝑍 is the criteria for the TPZ identification. 

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2.2.9. Validation of Models 

Validation is one of the most critical steps in ensuring the accuracy of any model (Mitra et al., 2022, 

Sofaer et al., 2019). There are several ways to validate a model and here, the overall accuracy, ROC-AUC and 

RMSE metric were used here. The ROC-AUC curve renders the trade-off between the False Positive Rate and 

True Positive Rate. The false positive rate is shown on the x axis of the ROC (e.g. a two-dimensional graph), 

while the true positive rate is shown on the y axis. Equation 11 and Equation 12 represents the attributes of x 

and y axis respectively: 

𝑥 = 𝑓𝑎𝑙𝑠𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑟𝑎𝑡𝑒 = 1 −
𝑇𝑁

𝑇𝑁+𝐹𝑃
 ……………. (Eq.11) 

𝑦 = 𝑡𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑟𝑎𝑡𝑒 =
𝑇𝑁

𝑇𝑁+𝐹𝑃
 ……………. (Eq.12) 

𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
(𝑇𝑁+𝑇𝑃)

(𝑇𝑁+𝑇𝑃+𝐹𝑁+𝐹𝑃)
 ……………. (Eq.13) 

where, TN, FP, TP, FN, and FP represent true negative, false positive; true positive; false negative; and the false 

positive values. The AUC (i.e., the area under the ROC curve) was applied to evaluate the performance of the 

AHP, LM, Elastic Net and KNN models. The proposed tourism potential map was verified high (Code 1) and 

moderate to low tourism potential points (Code 0). Further, the tourism potential map was validated using Root 

Mean Squared Error (RMSE). Lower RMSE depicts higher accuracy of the model. In this research, the RMSE 

was estimated using the following formula Equation 14: 

𝑅𝑀𝑆𝐸 = √
∑ (𝑥𝑖−𝑦𝑖)2𝑛

𝑖=1

𝑛
 ……………. (Eq.13) 

Where, 𝑥𝑖 is the observed value; 𝑦𝑖 is the predicted value. The number of observations is denoted as n. The ARC-

GIS 10.4 version, R and Python were used to process the data in this research.  

2. Result and Discussion 

3.1 Analysis of Criterion (Input Variable) 

a. Geology (GL) 

Overall, the Madhya Pradesh has nine geological units, which are Carboniferous sedimentary rocks, water, 

Cretaceous sedimentary rocks, quaternary sediments, Tertiary and Cretaceous sedimentary rocks, Paleocene 

Cretaceous extrusive rocks, Tertiary igneous rocks, lower Triassic to upper Carboniferous sedimentary rocks, 

and undivided Precambrian rocks Figure 4a. As the scenic beauty of water bodies attract tourists; it creates 

smooth visual ambience on tourists (36.7% weightage, Code 9). On the other hand, the undivided Precambrian 

rocks hold least or equal importance (9.6% weightage, Code 1). The Tertiary and Cretaceous sedimentary rocks 

are noticed with moderate importance (14.4% weightage, Code 3).  

b.  Aspect (AS) 

The Aspect was reclassified here into four classes; and as the class value decreases, the tourism potentiality 

is expected to increase and vice-versa. Following this, the lowest class was gained very strong to extreme 

importance (Code 8, 55% weightage). On the contrary, the highest class achieved the Equal to Moderate 

importance (Code 2, 9.4% weightage) Figure 4b. Other classes were marked with Strong to Very Strong 

importance and Moderate to Strong importance.  

c.  Relief (RL) 

The relief of the Madhya Pradesh fluctuated from 56 metre to 1333 metre. The northern, north-eastern 

and south-eastern sections of the study area were marked with lower relief (i.e., 56.00 -300 metre). The Narmada 

River is flowing from south-western to north-eastern section and hence this portion attains a moderate to low 

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relief (i.e., 300 metre - 400 metre). The north-western and south-eastern portions were marked with a higher 

relief value (400.01metre- 1333 metre) Figure 5a. Low relief is suitable for tourism activity (Ovreiu et al., 2018) 

and thus the lowest relief was noticed with higher priority and vice-versa.  

 
                Source: Authors, 2023 

Figure 4 a) Geological Map; b) Aspect Map 
 

 
           Source: Authors, 2023 

Figure 5. a) Relief b) Distance from River 
 

d.  Distance from River (m) (DR) 

The DR in the Madhya Pradesh varied from 0 to 0.170-meter Figure 5b. Riverine beauty increases the 

scenic beauty of a particular territory (Pouya & Başkaya, 2018). Therefore, tourism potentiality is positively 

enhanced by the scenic beauty of the river. Here, DR was classified into 4 classes; and as the class value increases; 

priority decreases and vice-versa. Hence, the lowest class (i.e., 0 to 0.042m) was marked with the highest 

weightage (48.7% weightage, Code 8) and the highest class (i.e., 0.128 to 0.170m) was marked with the lowest 

priority (5.5% weightage, Code 2). The lowest class was identified here with the highest areal coverage (%).  

e.  Road and Railway Density (RRD) 

The RRD fluctuated in the Madhya Pradesh from 0 to 453 Km./Sq. Km Figure 6a.  Excessively high RRD 

decreases tourism potentiality but the moderate to low RRD increases the value of tourism potentiality (Acharya 

et al., 2022). Here, the RRD has been subdivided into 4 classes and the highest class was identified with the 

highest priority and vice-versa.  

f.  Distance from Road and Railway (DRRD) 

The DRRD varied from 0 to 0.200 meter within the study area Figure 6b. If the distance from road and 

railway increases; tourism potentiality decreases and vice-versa (Dedík et al., 2022). Here, the DRRD was divided 

into 4 classes and the highest class was marked with the lowest weightage and the lowest class was marked with 

the highest weightage. The highest class was coded with 2 (Equal to Moderate importance) and lowest class was 

coded with 8 (Very Strong to Extreme Importance).  

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g.  Total Population (TP) 

The TP varied from 46768 to 3272335 within the study area (Figure 10a). The TP is comparatively low 

in the Datia, Sheora, Ashoknagar, Panna, Shahdol, Dindori, Anupur, Hadra, Burhanpur, Jhabua, Alirajpur and 

Neemuch districts. Higher TP (i.e., 2090307 to 3272335) was marked in the Satna, Rewa, Sagar, Jabalpur, 

Bhopal, Indore and Dhar districts Figure 7a. Comparatively low TP accelerates the scenic beauty and thus 

motivates the tourists to visit the Madhya Pradesh. Here, the TP was classified into 4 class, and as the class 

value decreases, priority increases and vice-versa.  

 
                 Source: Authors, 2023 

Figure 6. a) Road and Railway Density b) 
Distance from Road and Railway 

 

 
  Source: Authors, 2023 

 Figure 7. a) Total Population b) Growth Rate 

 

h.  Population Growth Rate (PGR) 

Low PGR value (i.e.,12.30% to 16.10%) was marked for the Chhindwara, Hoshangabad, Betul, 

Warshinghpur, Jabalpur, Balaghat, Anuppur, Ujjain, Mandasur, and Neemuch districts. The PGR is higher (i.e., 

20.21% to 32.70%) for the Guna, Bhopal, Singrauli, Indore, Barwani, Jhabua, Dhar, Khargone, Khandwa, Sehore, 

Rajgarh, Shivpuri, Ashoknagar, Gwalior, Sheopur, Morena, Katni, Umaria, Dindori and Sidhi districts Figure 

7b. Remaining districts are marked with 16.11% to 20.20% PGR. Higher PGR degrades the environment and 

thus deteriorates the tourism potentiality (Raha & Gayen, 2022b). Here, the PGR was reclassified into 4 groups 

and as the different class value of PGR decreases; tourism potentiality increases and vice-versa.  

i.  Sex Ratio (SR) 

The Sex Ratio (SR) of the Madhya Pradesh varies from 838 to 1021. Lower SR (i.e., 838 to 870) occurred 

for the Morena, Bhind and Gwalior districts. Higher sex ratio (i.e., 976 to 1021) was identified for the Jhabua, 

Alirajpur, Barwani,Seoni,Belaghat, Mandia and Dindori districts. Remaining districts were noticed with 871 to 

975 sex ratios Figure 8a. The higher sex ratio helps to flourish the tourism potentiality (Rahman, 2021) and 

therefore as the class value of SR increases; priority increases and vice-versa.  

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j.  Literacy Rate (LR) 

The Literacy Rate of the study area fluctuates from 37.20% to 82.50%. Comparatively low literacy rate 

(i.e., 37.20% to 68.40%) was marked for the Jhabua, Alirajpur, Barwani, Sheopur, Shivpuri, Guna,Ashoknagar, 

Rajgarh, Ratlam, Dhar, Khargone, Burhanpur, Khandwa, Tikmagarh, Chhatarpur, Panna, Mandla, Dindori, 

Shahdol, Umaria, Sidhi, and Singrauli districts. Higher LR was observed for the Datia, Morena, Vidisha, 

Anuppur,Sahajapur,Sehore,Dewas, Ujjain, Neemuch, Raisen, Betul, Damoh, Chhindwara, Seoni, Katni, Satna, 

Rewa, Bhind, Gwalior, Bhopal, Indore, Hoshangabad, Sagar, Narsinghpur, Balaghat, and Jabalpur districts 

Figure 8b. As the tourism potentiality is positively vibrated by the LR (Raha & Gayen, 2022a); the higher class 

of LR was marked with the higher priority and vice-versa.  

 
        Source: Authors, 2023 

Figure 8. a) Sex Ratio b) Literacy Rate 

3.2 Density of Tourist Spots (TS) (Target Variable) 

Density of tourist spots is a good indicator of tourism potential (Chen et al., 2021; Marrocu & Paci, 2013). 

The intensification of tourist spots increases the tourist availability and in turn creates destination loyalty. Many 

tourist spots create ample opportunity for tourists to identify many destinations within a single time budget 

friendly time frame. Total 487 tourist spots were identified in the Madhya Pradesh state; and those were 

categorized under 11 categories. Bhimbetka rock shelter is one of the most popular archaeological tourist sites 

explored here.  

Apart from it, the Bhind fort (castle), Maharana Pratap Square (memorial), Matageswar Temple 

(monument), Rani Roopmati Mahal (monument), hanuman Statue (monument), Tribal Museum, Darya Khan’s 

Tomb (Ruins) were explored. The region is very rich in several artwork (e.g, Raja Bhoj and Bhagawan Kala 

Kendra etc.), theatre centres (e.g. Davy auditorium, Bharat Bhawan amphitheatres), several attractive scenic 

beauty places (e.g., Purva falls, Udaigiri cavesRaneh waterfalls viewpoint etc.), water tower, Budhist (e.g. 

Southern gate, Western gate etc.), Christian (e.g. Nun Monestry), Hindu (e.g. Nilkanth Shiva temple, Iscon 

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temple), Muslim (e.g. Jama Masjid) and Sikh (e.g. Gurudwara) tombs. Café (e.g. Dominos Vijay Nagar, Cafe 

Coffee Day Cafe Kava, Chaifeteria etc.), restaurants (e.g. Mediterraneo Restaurant, My Kitchen Restaurant, Food 

Land Restaurant, Balaji Family Restaurant and Dhaba etc.), community centres (e.g., Akshat Garden, Relax 

Garden, Nani Maa Ki Dharamshala, Ravindra Bhavan, Hindi Bhavan, Gandhi Bhavan, Shahpura Community 

Hall, Sindhu Bhavan etc.), giftshop (e.g. handmade items), guesthouse (e.g. Sharma Guest House, Madai Forest 

Guesthouse), hotel (e.g., Gem Palace, Royal Garden, Red Maple, Pramad Palace etc.), motel, park (e.g., Kanha 

National Park, Rani Park, Saket Park, Dravid Nagar Colony Park,  Naveen Nagar Park, Laxman Sing Gaur 

Udyan etc.) picnic site (e.g., Ekta Park, Siddha Ghat etc.), sports centre, stadium (e.g., Railway Stadium, Cricket 

Stadium, Ashbagh Stadium, Dr Bhim Rao Amedkar Stadioum etc.) and several tourist information centres (e.g., 

Ticket Counter Man-Singh-Palace, Orchah Nature Reserve, Ticket Counter, Asi Counter, Government of India 

Tourist Office, Ticket Office, M.P. Tourism Office, Kanha National Park- Kisli Gate etc.),Supermarket (e.g. 

Vishal Mega Mart, Aparti Super market, Aprooti Super Market) were identified and listed with latitude and 

longitude (with the help of a GPS).  

Overall, the region is dominated by Guesthouses and hotels, followed by café, religious places, 

archaeological sites, giftshops and mall, park, community centres, tourist information centres, and several tourist 

viewpoints Figure 9b. The density of tourist spots is comparatively high at the Northern, South-western and 

middle South-north stretches. On the other hand, the South-western portions were marked with the relatively 

low density. Overall, 31.56% area of the Madhya Pradesh was marked as the highly dense with the popular 

tourist spots; and 68.44% area was identified as the low to moderately dense with several popular tourist spots 

Figure 9a. 

 
Source: Authors, 2023 

Figure 9. a) Density of tourist spots b) Frequency of different categories of tourist spots 

3.3 Analysis of Prerequisites 

a. Assessment of Multicollinearity 

The correlation matrix was portrayed in the Figure 4. It is evident that here the correlation coefficient 

value fluctuated from -0.49 to +0.17. As all of the correlation coefficient value comes below 0.8; it can be stated 

that no significant correlation coefficient value exists in the dataset. It was further verified from the scatter plot 

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in Figure 10. In most of the cases, the points are coalesced in different portions of the plot section. Therefore, all 

variables used in this research is independent and machine learning models can be applied on collected data 

without any hesitation. Further, before proceeding to prepare the ensemble model, the multi model inter-

collinearity was checked in Table 3. Here, the correlation coefficient fluctuated from 0.55 to 0.74. Therefore, 

models are not highly inter-correlated with each other and ensemble method could be applicable in determination 

of TPZs. 

Table 3. Correlation Matrix between Each Model 

Models AHP LM EN KNN 

AHP 1    

LM 0.6 1   

EN 0.59 0.69 1  

KNN 0.55 0.73 0.74 1 

 
Source: Authors, 2023 

Figure 10. Correlation Matrix 

b. Residuals vs. Fitted plot, Q-Q Plot, Scale location plot, Residuals vs. Leverage plot, Fraction Deviance 

Plot and Log Lambda vs. Coefficients Plot 

The red line seems to be fitted with the dashed line (parallel to the x-axis) in the residuals vs. fitted plot 

Figure 11a. In the Q-Q plot. Figure 11b the standardized residuals fit with the theoretical quantiles. Here all of 

the data points are exactly aligned over 45°line in the Q-Q plot. In the scale location plot Figure 11c, the residuals 

spread wider along with the x-axis. Moreover, the red line is almost aligned with the dashed line. That means 

the spread is random. There is no influential case found in the residual vs. leverage plot Figure 11d. Here, all 

data points are well inside the Cook’s Distance line.  The coefficients are large in both side of the axis, whenever 

the fraction deviance value is increasing Figure 12a. The coefficients are fluctuating from 0 to -0.25. The 

coefficients are increasing with increasing the log (𝜆) Figure 12b.  

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Source: Authors, 2023 

Figure 11. Different Plots a) Residual vs. Fitted; b) Normal Q-Q; c) Scale Location d) Residual vs. Leverage 

 
Source: Authors, 2023 

Figure 12. Different Plots a) Fraction Deviance; b) Log Lambda 

c. Best Tuning Results 

After the 5 repetition and 10-fold cross validation, here, optimal 𝜆 and α values were found. In this 

research, after the best tune the 𝜆 and α value was obtained as 0.0136 and 0.861 respectively. 

3.4 Estimation of Weightages by Each Model 

For the AHP; the GL and RL were marked with the highest weightage (23.8% and 21.6% weightages) and 

the Growth Rate (GR) (1.8% weightage) was marked with the lowest weightage. For the AHP, the AS was 

marked with 18.4% weightage; DR was marked with 11% weightage, DRRD was identified with 8.2% weightage; 

RRD was identified with 5.7% weightage; TP, LR and SR were marked with 4%, 3% and 2.5% weightages 

respectively Table 2. The layers were reclassified as 9, 8, 7,6,4,3,3,2 and 2 respectively. The GL and RL were 

marked with the highest code as those two layers were marked with the highest priority. Similarly, the SR and 

GR were identified with the lowest code (Code 2) as those were identified with the lowest priority. For the LM, 

KNN, and EN model, the RL was marked with the highest priority, and the LR was identified with the lowest 

priority. For the LM model, DRRD, DR, AS, GR, TP, SR, GL, were marked with 32.01, 18.80, 17.45, 11.70, 

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7.09, 4.98, 2.90, and 0.76 weightages. For the EN model, DRRD, DR, GR, AS, SR, GL, and RRD were identified 

with 23.14, 13.64, 12.07, 10.80, 5.56, 4.17, 0.57, and 0.02 weightages. Similarly, for the KNN model, SR, GR, TP, 

LR, RRD, GL, DR, DRRD, and AS were identified with 42.91, 15.80, 10.31, 7.47, 4.09, 1.77, 0.50, and 0.16 

weightages. For the ensemble model, GR, LR, SR, TP, AS, DR, DRRD, RRD, RL, and GL, were marked with 

6.40, 1.60, 8.40, 4.20, 7.20,6.80, 9.80, 1.60, 4.96 and 4.50 weightages Figure 13. The detailed weightages for the 

AHP and other models were portrayed in the Table 2. 

Table 2. Estimation of Priority of Different Indicators through AHP  

 

 
 Source: Authors, 2023 

Figure 13. Variable Importance Plot of different models 

3.5 Tourism Potential Zone (TPZs) 

For all models, the TPZs were classified into the High, Moderate and Low TPZs. Using the AHP model; 

31.92% area of the Madhya Pradesh was identified as the low tourism potentiality; 58.69% area was delineated 

under the moderate TPZ and 9.39% area was marked with high tourism potentiality Figure 14a. In case of the 

LM model, 18.71% area was marked with the low tourism potentiality, 58.20% area was marked with moderate 

tourism potentiality, and 23.09% area was demarcated as the high tourism potentiality Figure 15a. For the EN 

model, 12.83% area, 66.66% area and 20.51% area were demarcated as the low, moderate and high tourism 

potentiality respectively Figure 16a. With the help of the KNN model, 15.56% area was marked as the low 

tourism potentiality, 57.16% area with the moderate tourism potentiality and 27.28% area was identified as with 

high tourism potentiality Figure 17a. For the ensemble model, 23.19% area was demarcated as the low TPZ, 

65.11% area as the moderate TPZ, and 11.70% area was delineated as the high TPZ Figure 18a.  For each case, 

the Northern, South-western and middle South-north stretches were identified with the high tourism 

potentiality. On the other hand, the South-western portions in each case were marked with the relatively low 

tourism potentiality. The North-western portions are demarcated with the moderate tourism potentiality in each 

case Figure 14a, 15a, 16a, 17a. 

Parameters GL RL AS DR DRRD RRD TP LR SR GR 
Priority (%) 
Weightage 

GL 1 1 2 3 5 4 5 6 7 7 23.8% (9) 

RL 1 1 1 2 4 5 6 7 7 8 21.6% (8) 

AS 0.5 1 1 2 3 4 5 6 7 8 18.4% (7) 

DR 0.33 0.5 0.5 1 2 2 3 4 5 6 11% (6) 

DRRD 0.2 0.3 0.3 0.5 1 1 3 4 5 7 8.2% (5) 

RRD 0.25 0.2 0.3 0.5 1 1 1 2 3 4 5.7% (4) 

TP 0.2 0.2 0.2 0.33 0.33 1 1 1 2 3 4% (3) 

LR 0.17 0.1 0.2 0.25 0.25 0.5 1 1 1 2 3% (3) 

SR 0.14 0.1 0.1 0.2 0.2 0.33 0.5 1 1 2 2.5% (2) 

GR 0.14 0.1 0.1 0.17 0.14 0.25 0.33 0.5 1 1 1.8% (2) 

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In case of AHP based TPZ; the high tourism potentiality dominated at Chambal (34.37% area); Gwalior 

(15.46% area), Indoor (13.02% area), Rewa (11.84% area) and Sagar (16.77% area) subdivisions. For the LM 

model; the high TPZ was trounced at the Chambal (81.65% area), Gwalior (27.94% area), Indore (37.56% area), 

Narmadapuram (17.81% area). Rewa (32.11% area), and Sagar (32.75% area) subdivisions. Similar feature exists 

for the KNN and Elastic Net models also; but the percentage area differs slightly. For the KNN model, Chambal, 

Gwalior, Indore were identified with the higher tourism potential with 94.07%, 33.26% and 32.58% area 

respectively. Narmadapuram, Rewa and Sagar were marked with 29.11%, 23.48% and 56.84% area respectively 

under the high TPZ. For the Elastic Net model, Chambal, Gwalior, Indore, Nardapuram, Rewa, and Sagar were 

marked with 80.63%, 27.64%, 37.45%, 16.24%, 23.27% and 27.48% area Figure 14b, 15b, 16b, 17b. For the 

ensemble model, 18% districts were marked under the high TPZ category. For the Bhopal, Gwalior and Rewa 

each, 2 districts were categorized under the high TPZ Figure 18b, 18c. These sections have moderate relief, and 

a better accessibility and connectivity network through high road-railway density. Apart from it, the distance 

from the river, the total population and population growth rate are relatively less. The sex ratio and literacy rate 

are also high in these sections of the study area.   

 
Source: Authors, 2023 

Figure 14. a) TPZ by the AHP technique b) Sub-division wise TPZ 

In case of the AHP based model; the Low TPZ (LTPZ) dominated for the Jabalpur (47.83% area), 

Narmadapuram (48.43% area), Rewa (24.33% area), Sagar (24.75% area), Shadal (36.23% area), Bhopal (35.94% 

area), Gwalior (22.57% area) and Indore (24.41% area) subdivisions. The Chambal district are identified with the 

lowest areal coverage (4.09% area) of LTPZ. For the LM model; the low TPZ subjugates for the Bhopal (17.85% 

area), Gwalior (17.44% area), Indore (12.52% area), Jabalpur (37.68% area), Narmadapuram (40.39% area), 

Shahdol (22.45% area) and Ujjain (10.43% area) districts. The Chambal district are identified with the lowest 

areal coverage (1.63% area) of LTPZ. For the Elastic Net model, Jabalpur (31.02% area), Narmadapuram (37.71% 

area), Shadol (19.95% area) and Bhopal (8.09% area) districts were marked with higher LTPZ. For the KNN 

model, Indore (11.56% area), Jabalpur (64.04% area), Narmadapuram (36.78% area), and Shadol (17.13% area) 

were noticed with higher areal (%) coverage of LTPZ Figure 14b, 15b, 16b, 17b. Remaining districts were 

marked with less than 5% area in this category. For the ensemble model, 32% districts of the study area were 

categorized under the low TPZ. Approximately 2 to 4 districts of Bhopal, Indore, Jabalpur, Narmadapuram, and 

Shadol subdivisions were marked with low TPZ Figure 18b, 18c. 

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112 

 

Source: Authors, 2023 

Figure 15. a) TPZ by the LM model b) Subdivision wise TPZ 

 
Source: Authors, 2023 

Figure 16. a) TPZ by EN model b) Sub-division wise TPZ 

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Source: Authors, 2023 

Figure 17. a) TPZ by KNN model b) Sub-division wise TPZ 

In case of the AHP based model; the lowest Moderate TPZ (MTPZ) was marked (46.97% area) for the 

Narmadapuram District. Other districts were noticed with 58% to 65% areal coverage. For the LM, KNN and 

Elastic Net model, the Chambal district was marked with the lowest areal coverage (16.71% area; 5.93% and 

19.25% area). Remaining districts were identified with 40% to 80% areal coverage Figure 14b, 15b, 16b, 17b. For 

the ensemble model, approximately 50% area was demarcated under the moderate TPZ. Approximately, 2 to 6 

districts from each subdivision (except, Bhopal and Shadol) fall under this category Figure 18b, 18c. 

 
Source: Authors, 2023 

Figure 18. a) TPZ by Ensemble Model b) share of number of districts under different categories of TPZ 

in different subdivisions c) Percentage share of number of districts under TPZ 

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3.6 Accuracy Assessments 

 For the AHP model; the overall predictive accuracy for the training and test set were marked as 83.1% 

and 87.3% respectively. The AUC for the ROC curve for both training and test set were marked as 73.1% Figure 

19a and 73.4% Figure 19b respectively. For the LM model; the overall accuracy of the model was 90.6% for the 

training data and 81.4% for the test set. For the LM model, the AUC for the ROC curve for both training and 

test set were marked as 93.9% Figure 19c and 85.1% Figure 19d respectively.  

The overall accuracy for the Elastic net model were identified as the 90.3% and 86.5%, respectively for the 

training and test sets. For the Elastic Net model, the AUC for the ROC curve for both training and test set were 

marked as 93.3% Figure 19e and 85.5% Figure 19f respectively. For the KNN model, 83.5% and 85.7% accuracy 

were marked for the training and test data respectively. For this model the AUC were marked as 90.8% Figure 

19g and 88.3% Figure 19h respectively. For the ensemble model, the overall accuracy was attained as 91.2%for 

the training set and 89.1% for the test set respectively. RMSE for the ensemble model was achieved as 0.33 and 

0.46 for training and test set respectively. The AUC was marked as 94.5% area Figure 20a for the training set 

and 89.4% area for the test set Figure 20b. The combined RMSE was lowest for the ensemble mode (i., e., 0.79); 

whereas it was higher for the LM models. For the AHP; the combined RMSE value was moderate Table 4. 

Further, the combined AUC value was the highest for the ensemble model; followed by KNN, LM, EN and AHP 

model Table 5. Therefore, the ensemble model outperformed the others for the TPZ identification in this region. 

 
  Source: Authors, 2023 

Figure 19 AUC-ROC measurement for different models 

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Table 4. RMSE for each model 

Model RMSE for the Training Set RMSE for the Test Set Combined RMSE 

LM 0.36 0.96 1.32 

AHP 0.37 0.71 1.08 

KNN 0.37 0.46 0.83 

Elastic Net 0.37 0.5 0.87 

Ensemble 0.33 0.46 0.79 

Table 5. AUC for each model 

ROC-AUC value 

Model name AUC for the Training Set AUC for the Test Set 
Combined 

Level of Accuracy 
AUC 

Ensemble 94.50% 89.40% 183.90% Highest 

KNN 90.80% 88.30% 179.10%  

LM 93.90% 85.10% 179% To 

EN 93.30% 85.50% 178.80%  

AHP 70.10% 70.20%  140.30% Lowest 

 
Source: Authors, 2023 

Figure 20 AUC-ROC for the ensemble model a) Training Set b) Test Set 

 

 
Source: Authors, 2023 

Figure 21 Tourist Spot Identification by Non-Participant Observation Technique and Google Earth Imagery 

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3.7 Detailed Discussion and Implication of the Research  

The research has several implications via following significant aspects at first, theoretically this research 

provides an engaging real-world application of Leiper's Tourism System Theory (Leiper, 1990) and Michael 

Porter’s Diamond Model (Porter, 1998). The tourist, the generating region, the transit route, the destination 

region, and the tourism industry are the five main components that make up Leiper's model, which describes 

tourism as a dynamic system. The study successfully maps and quantifies these interrelated components 

throughout Madhya Pradesh utilising Multi-Criteria Decision Making (MCDM) approaches and machine 

learning algorithms, offering a methodical examination of how tourism operates as an integrated whole. Further 

this research implicates the Diamond Model of Tourism by evaluating and enhancing the regional 

competitiveness of tourism destinations through a structured, data-driven approach. The Diamond Model, 

adapted from Michael Porter’s original framework (Porter, 1998), comprises four key determinants—factor 

conditions, demand conditions, related and supporting industries, —that collectively influence a destination's 

tourism competitiveness.  

Through the application of Multi-Criteria Decision Making (MCDM) methods and machine learning 

algorithms, this study quantifies and spatially analyzes these determinants across the diverse regions of Madhya 

Pradesh. Factor conditions, including geology, relief, aspect, distance from river, cultural such as density of 

tourist spots, sex ratio, literacy rate, total population and infrastructural such as road and railway density, 

distance from road and railway are analyzed through geospatial and socio-economic information, determining 

areas with high tourism value. Demand conditions are indirectly considered by taking into account accessibility 

and connectivity, affecting tourist movements from domestic and international markets. Through the integration 

of these factors into a complete map of tourism potential zones, the research not only strengthens the theoretical 

model of the Diamond Model but also makes it more applicable to regional tourism planning. The inclusion of 

machine learning provides predictive and adaptive functions to the model, allowing stakeholders to foresee shifts 

in tourism demand and infrastructure requirements. Therefore, this study closes the gap between theory and 

practice by converting the conceptual dimensions of the Diamond Model into a working tool for destination 

development, competitive positioning, and strategic investment in Madhya Pradesh's tourism industry. 

Further, there are many studies on tourism in Madhya Pradesh, including resource development (Pandey 

et al., 2014), social media's impact on tourism (Gohil, 2015), art and craft tourism (Kumar et al., 2023), and 

tourism's economic effects (Sharma, 2019), role of mass tourism (Chandravanshi & Jain, 2023; Gohil, 2015) 

development of sustainable tourism sector (Kishnani, 2022), eco-tourism (Ahmad & Pandey, 2016) for the state 

of Madhya Pradesh, but hardly any research work is available on tourism potential zone identification on this 

tract. Therefore, the tourism potential zone identification for the state of Madhya Pradesh is a noble attempt. In 

case of AHP based TPZ; the high tourism potentiality dominated at Chambal (34.37% area); Gwalior (15.46% 

area), Indoor (13.02% area), Rewa (11.84% area) and Sagar (16.77% area) subdivisions. For the LM based TPZ; 

the high TPZ was trounced at the Chambal (81.65% area), Gwalior (27.94% area), Indore (37.56% area), 

Narmadapuram (17.81% area). Rewa (32.11% area), and Sagar (32.75% area) subdivisions.  

Similar feature exists for the KNN and EN models also; but the percentage area differs slightly. For the 

ensemble model, 18% districts were marked under the high TPZ category. For the Bhopal, Gwalior and Rewa 

each, 2 districts were categorized under the high TPZ. Higher tourism potential in any region is boosted by the 

low aspect (Vijay et al., 2016), moderate to low relief (Li et al., 2024), and closer proximity (small distance) to 

river water (Maaiah et al., 2023). Further, this higher tourism potential is amplified by moderate to low road and 

railway density (Sang et al., 2022) and the closest proximity (small distance) to road and railway (Rolando & 

Scandiffio,2022). These portions also marked with comparatively low total population (Chen et al., 2019), 

population density (Raha et al., 2021) but higher literacy rate (Chen & Li, 2023; Raha et al., 2021). Chambal, 

Gwalior, Indoor, Rewa and Sagar are identified with a popular tourist circuit (Olivelle, 2006).  

The starting point for this travel circuit is Indore. The Holkar kings' seat was this thriving trading town. 

From Delhi and Mumbai, it has excellent air, rail, and road connections. The Rajwada, the Palace, and the 

cenotaphs of the Holkar kings are some of its intriguing features. It is known as the "Mini Mumbai" due to the 

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significance of its business activity (The Market Research Division, Department of Tourism, 2003). Chambal is 

well-known for the Canoeing Safari or White-Water Rafting (Kohli, 2002). Gwalior features a tall citadel with 

14th-century AD Rajput residences and mediaeval monuments (Sijatha, 2017). An historic fort and cave provide 

a touch of heritage to Rewa's National Park which is known as the Bandhavgarh National Park (Lahiri et al., 

2022). It is the greatest location to witness tigers in their native environment (Lahiri et al., 2023). Sagar becomes 

extremely attractive in the monsoon because of scenic beauty (Rakhra, 2023). Those above-mentioned regions 

bear the state's cultural heritage, are dominated by handlooms. The Madhya Pradesh tourism Development 

Corporation also took necessary steps to popularise these portions such as, development of hotels, lodges and 

dormitories for providing accommodation to tourists, facilities of accessibility and connectivity through efficient 

transport networks, developing the tourist information centres, and advertising and marketing of tourist places 

(Ministry of Tourism Govt. of India, 2023).  

Additionally, the study used an integrated 9-step technique to identify tourist potential zones in the state 

of Madhya Pradesh. To the best of our knowledge, this study is a cutting-edge effort in the use of decision-

making and machine learning models for the accurate prediction of tourist potential zones. The research gives a 

detailed and analytical view on the on-the dynamics of tourism in the region by examining the spatial distribution 

of tourist potential levels, classifying them as high, moderate, and low. We performed a thorough evaluation and 

comparison of both traditional Multiple Criteria Decision Making (MCDM) methods, such as the Analytic 

Hierarchy Process (AHP), and modern machine learning models such as Elastic Net (EN), Linear Regression 

(LM), and k-Nearest Neighbours (KNN). It's important to highlight that although the Analytic Hierarchy 

Process (AHP) is well-acknowledged, the adoption of machine learning models EN, LM, and KNN for prediction 

of tourism potential zone identification has been widely appreciated by scholars worldwide.  

This research not only brings to the forefront the effectiveness of these various methodologies but also 

offers valuable perspectives on their real-world usefulness in forecasting tourism potential zones in the state of 

Madhya Pradesh. Furthermore, the intrinsic variety in ensemble model in this research acts as a buffer against 

the vagaries of uncertainty, protecting decision-making frameworks from the negative influence of outliers and 

noise. The ensemble model provides decision-makers with a more comprehensive and nuanced understanding of 

the underlying dynamics governing a given domain by leveraging the collective wisdom distilled from a variety 

of algorithmic perspectives, empowering them to make informed and judicious decisions amidst the tumult of 

uncertainty. Moreover, the synergistic interplay of constituent algorithms (i.e., AHP, LM, EN and KNN models) 

improve ensemble models' ability to infer complicated correlations contained within datasets, allowing for a more 

in-depth knowledge of underlying phenomena. This collective intelligence, created by the harmonic merger of 

many algorithms, generates a greater range of ideas, hence increasing the effectiveness of decision-making 

processes. 

3. Conclusion 

Madhya Pradesh is a very prominent and well-known tourist destination in India. The TPZ of Madhya 

Pradesh was explored in this research with the help of one MCDM (i.e., AHP), three machine learning (EN, LM 

and KNN models) and one ensemble models. The methodology was implemented here through 9 steps. First of 

all, total 11 layers (i.e., GL, RL, AS, DR, DRRD, RRD, SR, LR, TP, GR, and TS) was collected and prepared the 

raster layer in the GIS platform. Next, the multicollinearity was checked which is one of the basic prerequisites. 

Further residuals vs. fitted plot, normal Q-Q plot, residuals vs, leverage plot and scale location plot were checked. 

In each case, the trend line (red) was fitted with the dashed line, parallel with x axis. The residuals vs. leverage 

plot shows that there exist no influential significant extreme points exist in the dataset.  At the third step, the 

data was normalized to 0 to 1. The data was partitioned into training and test set in a 70:30 ratio at the fourth 

step. The TS was set as the target variable and the others were set as input variables in the fifth step. Next, the 

AHP, LM, EN and KNN models were applied in demarcation of TPZs. The ensemble model was prepared at 

seventh step by combining the AHP, LM, EN and KNN models. The proposed tourism potential maps were 

validated through the AUC-ROC curve and RMSE value.  

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The ensemble model appears as the best model as it was noticed with a low RMSE and higher AUC value. 

The northern, south-western, and middle regions emerge as high-potential areas, whilst the south-western edges 

were appeared with less potential. Meanwhile, the north-western expanse offers a scene of moderate potential. 

However, by adding more variables, the tourism potential map forecast accuracy may be further ameliorated. 

Furthermore, any alteration to the natural environment brought about by human activity or any changes in the 

natural phenomena such as, the relief, aspect, or distance from river, may alter the area's current status of tourism 

potentiality. Therefore, the tourism potential zone map should be updated annually by incorporating all the 

changes. The TPZ map can be used as the base data to support the planning and developmental activities in the 

state. To the best of our knowledge, this research is the first to highlight the tourism potentiality of the Madhya 

Pradesh using the decision making and machine learning models. This intricates the novelty of the research. 

This research   holds significant value in advancing sustainable tourism development by integrating Multi-

Criteria Decision-Making (MCDM) techniques and machine learning to identify and prioritize regions with high 

tourism potential. This innovative approach enhances spatial planning and resource allocation by combining 

expert-driven criteria assessment with data-driven predictive capabilities, offering a more accurate and dynamic 

mapping of tourism zones. The study not only aids policymakers and tourism stakeholders in making informed 

decisions but also contributes to regional economic growth, heritage conservation, and balanced tourism 

distribution across the state, aligning with broader goals of sustainable development and digital 

governance.Through the integration of machine learning and Multi-Criteria Decision-Making (MCDM) 

methodologies, the research effectively advances sustainable tourism development by identifying and prioritising 

regions with high tourism potential. By fusing data-driven prediction capabilities with expert-driven criterion 

assessment, this novel method improves spatial planning and resource allocation while providing a more dynamic 

and accurate mapping of tourism zones.  

The study supports balanced tourism distribution throughout the state, historical preservation, and 

regional economic growth in addition to helping policymakers and tourism stakeholders make well-informed 

decisions. These outcomes are in line with the larger objectives of sustainable development and digital 

governance. The TPZ map can be used as the base data to support the planning and developmental activities in 

the state. To the best of our knowledge, this research is the first to highlight the tourism potentiality of the 

Madhya Pradesh using the decision making and machine learning models. This intricates the novelty of the 

research. Future researchers should consider expanding the spatial and thematic resolution of datasets to 

improve model precision and scalability. They should integrate dynamic datasets such as real-time tourist 

footfall, social media sentiment, environmental change indicators, and transportation network updates to better 

capture evolving tourism patterns. Incorporating participatory GIS and crowd-sourced local knowledge can 

further enhance model accuracy and stakeholder relevance. Comparative analyses between different MCDM 

techniques (e.g., TOPSIS, PROMETHEE) and ML algorithms (e.g., Random Forest, Gradient Boosting, Neural 

Networks) should be systematically conducted to identify the most robust and context-sensitive combinations. 

Researchers are encouraged to explore ensemble modeling approaches to minimize uncertainty and enhance 

predictive validity. Future studies should also address the interpretability and explainability of ML outputs to 

facilitate practical implementation by policymakers and tourism planners. Furthermore, integrating 

sustainability indicators—ecological, cultural, and socioeconomic—into the TPZ framework can ensure that 

development strategies align with long-term conservation goals. Finally, establishing a temporal component in 

TPZ models to analyze seasonal variations and long-term trends could significantly improve planning 

effectiveness and the adaptability of tourism strategies in Madhya Pradesh. 

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