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            Geoplanning: Journal of Geomatics and Planning, Vol. 12, No. 1, 2025, 45 - 56 

 

Original Research 

Modeling the Water Quality of Lake Itasy by 

Long Short-Term Memory (LSTM) using 

Landsat 8 Data 

Randrianiaina Jerry Jean Christien Frederick1,2*, Rakotonirina Rija 

Itokiana1, Jean Robertin Rasoloariniaina², Fils Lahatra Razafindramisa1  

1. Laboratory of Matter and Radiation Physics (LPMR), University of Antananarivo, 

Madagascar 

2. Institut d’Enseignement Supérieur d’Antsirabe-Vakinankaratra, University of Antananarivo, 

Madagascar 

DOI: 10.14710/geoplanning.12.1.45-56 

Abstract 

Modeling lake water quality is very important to preserve and protect this resource. Several algorithms can be used to 

model lake water quality using in-situ measurement data. This work used The Long Short-Term Memory (LSTM) deep 

learning (DL) architecture to obtain models for modeling and predicting water quality parameters of Lake Itasy depending 

on the reflectance of Landsat8 OLI. The main purpose of this study was to identify the appropriate LSTM model in function 

of the optimization algorithms: Adagrad, RMSprop and Adam, in order to do the estimation on the date provided, according 

to the date of satellite image acquisition. The obtained results showed the performance of the developed LSTM model, with 

an Adaptive Moment Estimation (Adam) optimization algorithm that provided an excellent concordance between the 

collected and simulated water quality parameters. Moreover, the correlation coefficient (R²) was 0.993 for the conductivity 

and 0.977 for the dissolved oxygen concentration. The root mean square error (RMSE) values for conductivity and dissolved 

oxygen concentration were 0.898 and 0.228 respectively.  After choosing the best model, the water quality parameters of 

the Lake Itasy were estimated on May 25th 2020. The conductivity ranged from 46.8 µS.cm-1 to 66.5 µS.cm-1, and the 

dissolved oxygen concentration from 6.5 mg/L to 9.1 mg/L. These values indicate that the water from Lake Itasy respects 

the Malagasy norms in terms of conductivity and dissolved oxygen concentration 

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  

In Surface waters, such as the sea, rivers and lakes are important for long-term economic development and 

environmental sustainability (Al-Shaibah et al., 2021; Nagy-Kovács et al., 2019). However, the prediction of lake 

water quality has attracted the attention of researchers due to its effect on the life of biodiversity. Therefore, 

modeling lake water quality is necessary to monitor, preserve and manage this resource. Modeling water quality 

with traditional methods, such as in situ-measurements, sample collection from the site and laboratory analyses 

is impractical. These methods are very expensive, time consuming and limit the assessment of spatial and 

temporal trends of the water quality (Jerry et al., 2019).  

In this study, the conductivity of Lake Itasy water and its dissolved oxygen concentration were modeled. 

Lake Itasy is the third largest lake in Madagascar (approximately 35 km²) and plays an important role in the 

economic development of the Itasy region, thanks to tourism, which brings benefits to the local population 

through related activities, and fish farming (I. et al., 2020). Therefore, it is necessary to seek a new approach to 

e-ISSN: 2355-6544 
 
Received:  05 December 2023; 
Revised:  13 September 2024; 
Accepted:  09 May 2025; 
Available Online: 14 May 2025; 
Published:  26 May 2025. 
 
Keywords:  
LSTM, Landsat8, Water Quality, 
Itasy Lake 
 
*Corresponding author(s)  
email: r2jcfjerry@gmail.com  
 

 

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mailto:r2jcfjerry@gmail.com


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46 

avoid the limitations of the traditional monitoring method, as mentioned above, in order to preserve and protect 

this resource.  Remote sensing techniques and machine learning offer opportunities and approaches for modeling 

and monitoring lake water quality. Remote sensing modeling of lake water quality requires the establishment of 

a reliable relationship between surface reflectance measured by remote sensing and water quality parameters 

collected in situ (Barrett & Frazier, 2016). 

Previous research on water quality such as  Srivastava et al. (2024), are still focused on utilizing the visible, 

Infrared (NIR), and Thermal Infrared (TIR) spectra to detect water characteristics such as suspended sediment 

and algae growth. Then Ness et al. (2025), the development of mathematical models based on remote sensing 

data has been carried out extensively to analyze more than 26,000 water samples to understand the dynamics of 

environmental parameters. As well as research conducted by Gao (2024) which focused on monitoring industrial 

and agricultural waste pollution to see the quality of the aquatic environment. However, most of the research 

that has been done in the past is limited to spectral analysis approaches and the development of conventional 

statistical or regression models. Another studies, which have been conducted in Lake Itasy, utilized deep learning 

to analyze water quality including parameters such as Ph and turbidity (Jerry et al., 2019), as well as the use of 

Landsat 8 in water surface temperature monitoring which showed results that normal temperatures lie in the 

range of 18.1°C to 22.6°C (Jerry et al., 2018). Based on previous research, the application of Long Short-Term 

Memory (LSTM) on environmental research has become more and more extensive (Huang & Kuo, 2018) due to 

its good performance in time-series prediction , but no one of that using this model to water quality assessment.  

The application of deep learning models such as LSTM in the use of machine learning techniques, is still 

relatively rare in quality modeling, especially in Lake Itasy. Based on that gap, it found that there has been no 

research that specifically utilizes the use of deep learning LSTM model in Itasy Lake. The use of LSTMs can 

provide a deeper understanding of temporal patterns in environmental data that cannot be fully captured by 

static models or traditional approaches. To enrich the knowledge about implementation of LSTM model, this 

study aimed to convey the improvement of water quality model using LSTM to show the new development of 

LSTM based on deep neural networks, branches of machine learning. In detail, the model utilized Landsat 8 

satellite imagery to get some advantages of free and open-source data. 

2. Data and Methods 

2.1. Study Area 

Lake Itasy is located in the Itasy region, the Miarinarivo district and the rural commune of Ampefy. It is 

sited within the volcanic field of Itasy and geographically located between 19° 04’ latitude South and 46° 47’ 

longitude East (Figure 1). 

 
Figure 1. Location of the Study Area  

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2.2. Data Used 

In this study, images from the Landsat8 satellite were used. They were obtained the same day as the in-

situ measurements of the water quality parameters of Lake Itasy. The images can be freely downloaded from the 

United States Geological Survey (USGS) website. Landsat8 carries two sensors: OLI (Operational Land Imager) 

and TIRS (Thermal Infrared sensors). The characteristics of Landsat 8 OLI/TIRS are given in Table 1. 

Table 1. Landsat 8 Characteristics  

Band Designations Wavelength (µm) Spatial Resolution (m) 

Band 1 (Coastal Aerosol) 0.43 – 0.45 30 

Band 2 (Blue) 0.45 – 0.51 30 

Band 3 (Green) 0.53 – 0.59 30 

Band 4 (Red) 0.64 – 0.67 30 

Band 5 (Near Infrared) 0.85 – 0.88 30 

Band 6 (Short wave infrared) 1.57 – 1.65 30 

Band 7 (Short wave infrared) 2.11 – 2.29 30 

Band 8 (Panchromatic) 0.50 – 0.68 15 

Band 9 (Cirrus) 1.36 – 1.39 30 

Band 10 (Thermal infrared) 10.6 – 11.19 100 

Band 11 (Thermal infrared) 11.50 – 12.51 100 

Source: Landsat 8 Data Users Handbook, 2015 

2.3. Water Quality Parameters 

2.3.1. Conductivity 

Conductivity is the capability of the water to carry electric current and serves as a tool to assess the purity 

of water (Murugesan et al., 2006).  The ions like chloride −( )Cl , sodium +( )Na , calcium +2( )Ca , phosphate −3

4( )PO  

etc. are responsible for electric current conduction. 

2.3.2. Dissolved Oxygen  

Dissolved oxygen is one of the key parameters in water quality analysis. It is used by most aquatic 

organisms to survive (Gholizadeh et al., 2016). Dissolved oxygen concentration is influenced by the temperature, 

the rate of photosynthesis, the turbidity, and the concentration of organic matters, such as industrial waste (Arief, 

2017). 

2.4. Method 

2.4.1. Image pre-Processing 

The Dark Object Subtraction (DOS1) atmospheric correction method was used in this work to reduce the 

atmospheric effects and to calculate the values of surface reflectance. This method can provide an accurate 

mapping for wetland areas and is well accepted by the geospatial community (Song et al., 2001). The Quantum 

GIS (QGIS) software was used to carry out the DOS1 atmospheric correction and to draw the maps shown in 

this article. 

2.4.2. Long Short-Term Memory (LSTM) 

The deep neural network is the branch of machine learning introduced to approach the objective of 

automatic learning. These methods were developed in the 1980s but were quickly left because they were 

considered as not promising (Blier, 2017). With the introduction of computers, which can make quick 

calculations, new big data databases, and the progress in the optimization techniques, deep learning has become 

the most competitive algorithm for different tasks (LeCun et al., 2015), such as voice recognition and face 

recognition.  In deep learning, there are many architectures that can be used, such as Convolutional Neural 

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48 

Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) which was 

introduced by Hochreiter and Schmid Huber in 1997 (Hochreiter & Schmidhuber, 1997) to remedy the vanishing 

and exploding gradient problem that is encountered in RNN. LSTM was the architecture used in this study. 

a. The LSTM Architecture 

As an improvement of recurrent neural network, LSTMs are capable of learning long-term dependencies 

and remembering past information, while predicting future values and taking them into account (Hochreiter & 

Schmidhuber, 1997) LSTMs that use purpose-built the memory cells to store information also have this chained 

in similar structure, but the repeating module is structured differently (Liu et al., 2019). There are four 

interacting layers in an LSTM cell (Olah, 2015) as depicted in Figure 2. 

 
Source: Ping Liu et al. (2019) 

Figure 2.  LSTM unit structure  

Forget gate ft  (Equation 1) (Aslam et al., 2019): It decides what kind of information will be removed 

and how much information will be kept (Hafezparast Mavadat & Marabi, 2021). This process was carried out by 

the application of the sigmoid function σ  which takes the preceding output value −1th  at −1t  and the input 

value tx  att . The output value of ft   varies from 0 to 1. 

 1( , )t f t t ff W h x b−=  +σ ………… (Eq. 1) 

Input gate ti  (Equation 2) (Liu et al., 2019): It acts as a filter because it decides which information will be 

added to the memory cell. It takes the values 1th −  and tx , and its value is also included between 0 and 1 by using 

the sigmoid function . 

 1( , )t i t t ii W h x b −=  + ………… (Eq. 2) 

Memory cell: It contains two elements, which are the new memory cell 
tC  (Equation 3) and the final 

memory cell tC  (Equation 4). The new memory cell is used to carry the information which comes being’s updates 

and the final memory cell is used to update the state of the new memory cell. 

                   1tanh( , )t c t t cC W h x b−=  + ………. (Eq. 3) 

            1t t t t tC f C i C−=  +  …………. (Eq. 4) 

Here, tC  and 
1tC −

 are the cell states at times t  and −1t . 

The output gate ot  (Equation 5) (Liu et al., 2019):’It decides which information can be sent to the output 

of the cell state. Finally, the output of the LSTM unit was obtained by multiplying the output gate by the 

hyperbolic tangent of the cell state (Equation 6). 

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49 

 ( )1 ,t o t t oo W h x b −=  + ………….. (Eq. 5) 

tanh( )t t th o C=  …………. (Eq. 6) 

Note that
fW , iW , cW , oW are the matrix weights associated with f , i ,c ,o .  The terms

fb , ib , cb , ob are the 

biases that were obtained during the training and testing process. The sign * represents the matrix 

multiplication and   indicates the multiplication element by element.  

b. Optimization Algorithm 

Three optimization algorithms based on the gradient descent were used to optimize the deep neural 

network parameters, i.e. to update network weights iterative in training data: Adagrad (Adaptive gradient 

method) (Wang et al., 2019), RMSprop (Root Mean Square Propagation) (Hinton et al., 2011) and Adam 

(Adaptive moment Estimation) (Kingma, 2014). 

c. Activation Function 

The Rectified Linear Unit (ReLU) proposed by Nair and Hinton in 2010 was the activation function used 

in this work. ReLU is the most widely used activation function in the deep learning network (Nair V. & Hinton, 

G.E., 2010). It has proved to be the most successful and offers the best performance (Dahl et al., 2013; Zeiler et 

al., 2013).  It is given by the following Equation 7: 

 

……. (Eq. 7) 

 

2.4.3. Model evaluation 

A model without validation criteria is not a model. The performance of the models used in this study was 

evaluated according to two common evaluation indicators: The correlation coefficient (
2R ) and the root mean 

square error ( )RMSE . 

a. The Correlation Coefficient 

The correlation coefficient measures the relationship degree between two variables. Its value ranges 

between -1 and +1. If it is near -1 or close to +1, it indicates that the correlation between two variables is strong. 

It is given by (Equation 8). 

2 1

2 2

1 1

( )( )

( ) ( )

n

i i

i

n n

i i

i i

t t y y

R

t t y y

=

= =

− −

=

−  −



 
…………….. (Eq. 8) 

Where it  and t  represent the in-situ measurements and their average, iy  and y  represent the obtained values 

from the model and their average, n  is the observation number. 

b. Root Mean Square Error (RMSE) 

RMSE is often used to calculate the difference between the predicted value and the observed value. The 

smaller the RMSE value, the smaller the distance between the two values (prediction and observation). RMSE 

can be calculated using Equation 9. 

, 0

( ) max(0, )

, 0

x if x

f x x and

x if x




= = 
 

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50 

2

1

( )
n

i i

i

t y

RMSE
n

=

−

=


………………………… (Eq. 9) 

where it  is the in-situ measurement of the value of the water quality parameter, iy  is the prediction from the 

models and n  is the observation number. 

2.4.4. Research Diagram 

A summary of the methodology used to conduct this research is shown in Figure 3. The images used in this 

study were Landsat8 imagery, downloaded from USGS. When the data were obtained, the pre-processing 

included the atmospheric correction, and the extraction of the study area was performed on the QGIS software. 

Then, the bands 2,3,4,5 were selected and these pixels were extracted because the spectral response for these 

bands is related to water quality (Jerry et al., 2019). The pixels extracted from the in-situ measurements data 

were integrated into the LSTM model to train the network. The following step was to evaluate the performance 

of the LSTM model using RMSE and R2 to choose the appropriate model that will be used to estimate the 

conductivity and the dissolved oxygen in order to create the map distribution of these parameters.  

 

Figure 3. Research Flow Chart of the Study 

3. Result and Discussion 

This section presents the findings derived from the application of the LSTM model to predict water quality 

parameters, specifically conductivity and dissolved oxygen, based on remote sensing data. The discussion also 

covers the interpretation of the results in relation to the objectives of the study. The performance of the model 

is evaluated using RMSE and R² metrics to assess the accuracy and reliability of the predictions. The results are 

organized in subsections to clearly explain the model output and its implications. 

3.1. Result 

3.1.1. Conductivity 

The LSTM architecture deep neural network used for modeling the conductivity of Lake Itasy water was 

constituted of 20 inputs, 3 LSTM layers containing 15, 24 and 16 elements respectively, followed by 2 dense 

layers of 20 and 10 elements, and by 20 output layers. The number of epochs was 10000.  

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51 

  
Figure 4.  In-situ Measurements and Conductivity 

Predicted using the Adagrad Optimization 
Figure 5.  In-situ Measurements and Conductivity 

Predicted using the RMSprop Optimization 
 

The curves in the Figure 4 show the in-situ measurement conductivity and the predicted conductivity 

from this architecture with the Adagrad optimization. The correlation coefficient between the two values was 

0.500. It can be concluded that the model is inadequate.  The Figure 5 shows the measured conductivity and the 

predicted conductivity when the RMSprop optimization was used. This is an acceptable model with a 

correspondence rate of 85.9% between the in-situ values and the predicted values.  

 

Figure 6.  In-situ Measurements and Conductivity Predicted using the Adam Optimization 

After changing the optimization algorithm to Adam, the similarity between measured conductivity and 

predicted conductivity was obtained as shown in the Figure 6. With a 99.3% correspondence between two values, 

the model is appropriate. 

The performance of the model according to the optimization algorithm is given in Table 2. According to 

this table, the best optimization algorithm that can be used to model the conductivity of Lake Itasy is the Adam 

optimization algorithm since the correlation between collected values and estimated values is high (R²=0.993) 

and the RMSE value is weak (RMSE=0.898).  

Table 2. Model Performance 

Optimization Algorithm R² RMSE 

Adagrad 0.500 6.940 

RMSprop 0.859 4.432 

Adam 0.993 0.898 

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52 

The Figure 7 shows the estimation of the conductivity of Lake Itasy using LSTM with the Adam 

optimization. The spatial distribution of water conductivity ranged from 46.8 µS.cm-1 to 66.5 µS.cm-1. It 

indicates that the Lake Itasy water respects the Malagasy norm which is inferior to 250 µS.cm-1, and that it is of 

good quality (Class A) (Water surface classifications according to decree n°2003/464 in 15/04/03). 

 

Figure 7.  Map Distribution of the Conductivity of Lake Itasy (05/25/2020)  

3.1.2. Dissolved Oxygen 

On the other hand, for the modeling of the Lake Itasy dissolved oxygen concentration, an architecture 

composed of 18 inputs, 3 LSTM hidden layers of 33, 23 and 15 elements, 2 dense layers of 17 and 15 elements 

and one output layer of 18 elements was constructed. The number of iterations was 10000. 

  

Figure 8.  In-situ Measurements and Dissolved 
Oxygen Concentration Predicted using the Adagrad 

Optimization 

Figure 9. In-situ Measurements and Dissolved 
Oxygen Concentration Predicted using the 

RMSprop optimization 
 

The dissolved oxygen concentration measured in situ and the dissolved oxygen concentration estimated 

using the Adagrad optimization are represented in Figure 8. It is an acceptable model as the correlation 

coefficient between measured values and predicted values is 0.775. The Figure 9 shows the measurements of the 

dissolved oxygen concentration collected in situ and the concentrations predicted by replacing the Adagrad 

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53 

optimization by RMSprop. According to this figure, the concentrations measured are almost similar to the 

concentrations estimated, with a 92.5% correspondence.  Thus, this combination can be considered to be a good 

model. The curves in the Figure 10 show the collected data on the dissolved oxygen concentration and the 

estimations when the optimization is Adam. They are in good concordance, with a correlation coefficient of 0.997, 

the model is then adequate.   

 
Figure 10. In-situ Measurements and Dissolved Oxygen Concentration Predicted using the Adam 

Optimization 

Table 3. Model Performance 

Optimization Algorithm R² RMSE 

Adagrad 0.775 0.683 

RMSprop 0.925 0.417 

Adam 0.977 0.228 

Table 3 depicts the performance of the model according to the optimization algorithm. This table indicates 

that the LSTM architecture utilizing the Adam optimization is the most adapted model to estimate the dissolved 

oxygen concentrations of Lake Itasy. 

  

Figure 11.  Map Distribution of Dissolved Oxygen of Lake Itasy (05/25/2020) 

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The spatial distribution of surface water dissolved oxygen of Lake Itasy is shown in the Figure 11 after 

applying the best model, dated May 25th 2020. Regarding the Lake Itasy water class, it is good because the 

concentration of dissolved oxygen is superior to 5 mg/L everywhere (Water surface classifications according to 

the decree n°2003/464 in 15/04/03). 

3.2. Discussion 

          The focus of the present study was to compare the performances of the LSTM model when the 

optimization algorithm was changed while keeping the numbers of LSTM layers and the epoch number. The 

results showed that the LSTM model using a Rectified Linear Unit (ReLU) activation function and the Adam 

optimizer was the best model with a high-level R². This result is in accordance with the research of Dheda & 

Cheng (2020) based on both multivariate single and multiple step LSTM models using ReLU activation and 

RMSprop optimizer, and confirmed the abilities of the LSTM model in water quality prediction (A et al., 2021; 

Liu et al., 2019; Wang et al., 2023; Wang et al., 2017). One weakness of this work was that the number of data 

collections was little.  

This study is a further response to various studies that have been conducted previously by Gao (2024); 

Jerry et al. (2018, 2019); Ness et al. (2025); and Srivastava et al. (2024). Although these studies have made 

important contributions to water quality assessment through the use of remote sensing and deep learning 

models, none have specifically utilized the use of LSTM in temporally modeling water quality. The use of LSTM 

in this study provides a new perspective due to its ability to capture the patterns and dynamics of time series 

data that are relevant in monitoring changes in water quality over time. As such, this approach not only 

complements previous research, but also offers the potential to improve and understand long-term fluctuations 

in water quality. 

Based on the results of the research that has been done, it can be seen that the advantages of the results 

obtained using LSTM show results that are close to real conditions. Possible future works include improving 

the number of in situ measurements and expanding the study to incorporate ensemble modeling techniques, 

combining LSTM with another deep learning architecture, such as CNN (Barzegar et al., 2020), RNN, to further 

improve the accuracy and robustness of the predictions. This is not only useful for environmental management, 

but can be maximized for infrastructure provision planning in supporting the availability of water resources. 

4. Conclusion 

Lake biodiversity plays an important role for aquatic life, for humans and for appropriate development. In 

Madagascar, surface waters are used for human consumption after treatment and for other activities, such as fish 

farming. To protect this resource, modeling about the quality of the water is necessary. This work showed that 

the Long Short-Term Memory (LSTM) with the Adam optimization was the most appropriate model for 

modeling and predicting the quality of the Lake Itasy water using remote sensing (Landsat 8). This model 

reached a high level of correlation coefficient R²>0.95 for the conductivity and the dissolved oxygen 

concentration, which is trustworthy. The obtained results indicate that Lake Itasy respects the Malagasy norms 

regarding conductivity and dissolved oxygen concentrations. This work is a contribution to avoiding the 

problems associated with the traditional method of monitoring water quality, and to enabling the monitoring 

and evaluation of changes in conductivity and dissolved oxygen in the surface water of Lake Itasy. The two 

parameters that were modeled in this work are among the key parameters for determining surface water quality. 

Therefore, it is highly recommended that future research model other water quality parameters such as turbidity, 

pH, temperature etc. to gather more knowledge about this lake.  

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