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Flood Vulnerability Mapping Using Geographic Information 

System (GIS) in Gajah Wong Sub Watershed Yogyakarta 

 

A. Ardiansyah¹* and  Dyah Respati Suryo Sumunar² 

¹Geography Education Master Program, Yogyakarta State University, Jl. Colombo No.1, 

Karang Malang,  Yogyakarta, 55281, Indonesia 

²Department of Geography Education, Yogyakarta State University, Jl. Colombo No.1, 

Karang Malang,  Yogyakarta, 55281, Indonesia 

*Corresponding Author : ardiansyah.2016@student.uny.ac.id  

Received 18 March 2019/ Revised 18 September 2019/ Accepted 23 November 2019/ Published 10 April 2020 

 

Abstract 

Gajah Wong Sub Watersheds frequently hit by floods which are potentially damaging. 

Therefore, a study on a flood vulnerability of the area is deemed necessary. This study aims 

to map floods vulnerability, to know the level and its spread in Gajah Wong Sub Watershed 

of Yogyakarta County Province by using Geographic Information System (GIS). The 

methods implemented in this study was weighting and scoring analysis and overlay of 

parameter attributes data of flood vulnerability framer, consisting of land use, slope of 

mountain, rainfall, soil type, geology, height of location and river buffer. Each parameter of 

flood vulnerability framer is classified based on the magnitude of effect towards flood 

vulnerability. The results of this study indicate that there were three levels of flood 

vulnerability in  Gajah Wong Sub Watershed, i.e. low flood vulnerability of 338.34 Ha 

(6.86%), medium flood vulnerability level of 4,595.62 Ha (93.13%) and high flood 

vulnerability level of  0.76 Ha (0.02%). Low flood vulnerability level is ditributed randomly 

to all areas of  Gajah Wong Sub Watershed cover of Ngaglik Sub-district, Depok Sub-district, 

small part of  Pleret Sub-district and was predominantly in  Banguntapan Sub-district, an area 

with rainfall. Medium flood vulnerability areas dominated Gajah Wong Watershed. 

Meanwhile, high flood vulnerability level occupied small portion of the area and spread in 

the southern part of Pleret Sub-district which was taken as the area of River Buffer analysis. 

Keywords: GIS; Gajah Wong; Sub Watershed; Yogyakarta. 

 

1. Introduction 

Climate change has seriously affected the environment and human life. Change in 

climate would contribute to increasing frequency and severity of disaster and cause flood (Jia 

et al., 2016). Generally, flood is caused by natural and non natural factors.  Flood occurs as a 

result of heavy rain effect and natural water channel having no capacity to collect water 

(Ramdhan et al., 2018). Area with lowland condition and near a river tends to have higher 

GEOSFERA INDONESIA 
p-ISSN 2598-9723, e-ISSN 2614-8528  Vol.5 No. 1 (2020), 47-64, April, 2020 

https://jurnal.unej.ac.id/index.php/GEOSI 

DOI : 10.19184/geosi.v5i1.9959 

Accredited by the Ministry of Research, Technology, and Higher Education of the Republic of Indonesia, No. 30/E/KPT/2019. 

 

 

 

 

https://jurnal.unej.ac.id/index.php/GEOSI
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floods vulnerability (Kourgialas & Karatzas, 2016). Flood disaster may have negative effects 

on social-economic, loss of human and properties, problems associated with health, and 

ecosystem functions (Sein & Myint, 2016).   

Gajah Wong Sub Watershed covers an area of 49.34 km², consisting of three areas of 

district/city, namely, districts of Sleman, Yogyakarta City and Bantul which cover 9 areas of 

sub-districts, i.e. Pakem, Ngemplak, Ngaglik, Depok, Umbulharjo, Kota Gede, 

Gondokusuman, Bantul, and Pleret (Hartono, 2014). Observation results indicate the frequent 

occorrence of annual floods in Gajah Wong Sub Watershed, with some major floods which 

may occur twice a year.  These frequently occurring floods were caused by overflow of Gajah 

Wong River and these have cause damages to houses and some other losses. 

Flood is considered one of the most common natural disasters contributed to losses and 

damage to infrastructure and the environment (Santillan et al., 2016). Flood disasters carry 

serious implications for humans; hence, there is a crucial need to manage and determine areas 

having flood vulnerability (Stoica & Iancu, 2011).  

Vulnerability is associated with a condition determined by social, economical, and 

ecological factors or processes, resulting in higher vulnerability of people in dealing with 

hazard (Christiawan & Wesnawa, 2014). Social factors include social conditions such as 

population density, sex ratio, poverty ratio, disability rate, and the ratio of vulnerable age 

groups. Economic factors include the condition of productive land area and gross regional 

domestic product (GRDP). Environmental vulnerabilities generally relate to land use 

conditions (BNPB, 2012).Vulnerability concept is generally associated with natural hazard, 

disaster and ecology (Beevers & Strathie, 2016). 

Knowledge and understanding of flood vulnerability are paramountly crucial for the 

government and people. Map is a good instrument to present data and information. Mapping 

areas with flood vulnerability interval is essential as it can be the basis for the government to 

make appropriate policy to prevent it. GIS provides support especially for policy and decision 

makers. Flood can be mapped by Attribute Analysis using weighing and scoring 

methodaiming at giving specific values to spatial data phenomena. This mapping activity 

aims at knowing the effects of components on flood vulnerability in Gajah Wong Watershed. 

Geographic Information System (GIS) is an integrated software, designed specially to 

be used with geographic data to perform tasks of data processing comprehensively consisting 

of input, storage, retrieving and output of data with various analytical and descriptive types 

(Weng, 2010). Recent disaster analyses are inseparable from GIS and the role of remote 

sensing. The use of GIS with remote sensing combination can widely be used and applied to 



             

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analyze disaster with provision of spatial data networks nationally and entirely (Manfré, 

2012). With these attributes, GIS may be functional and may have positive impacts on human 

life. GIS can be an essential tool in decision making of sustainable development, because GIS 

provides information to decision making for spatial database and analysis (Suryantoro, 2009). 

Previous related research stated that spatial databases include flood inventory, slope, 

flow power index (SPI), Topographic Wet Index (TWI), altitude, curvature, distance from 

rivers, geology, rainfall, Land Use / Cover (LULC), and types soil (Tehrany et al., 2014). 

Remote sensing is effective for analysing trends in environmental change related to 

biodiversity in recent years (Pettorelli et al., 2014). Lowland with 10-15% slope and with a 

sewage disposal network is the most vulnerable to flooding (igović et al., 2017). Analysis 

related to geology and distance from the river using digital elevation models  is most 

influential on reducing flood risk (Lee et al., 2017). The distance from the fault and the type 

of soil also greatly affects the level of flood vulnerability (Tehrany et al., 2017). 

Previous studies did not examine flood vulnerability in  karst and quarterly volcanic 

areas such as those in Yogyakarta. Therefore, this  study carried out on watersheds with a 

combination of quarter volcanic land and karst land in the provincial capital contributes in 

this regard. This study aims to map the vulnerability of floods to know the level and its 

spread in  Gajah Wong Sub Watershed of Yogyakarta County by using a Geographic 

Information System (GIS). 

2. Methods 

This is a descriptive quantitative study with the aims to disclose facts available 

accompanied by interpretation and analysis (Tika, 2005). This use of quantitative methods 

was mainly because data in this research is mainly attribute data that is dominated by 

numerical data.The data are used to carry out weighting, scoring and overlay analysis to 

produce flood vulnerability maps.This study aims to map flood vulnerability by viewing the 

level and its spread through results of flood vulnerability paramater data. This study aimed to 

provide reliable and accurate results.  

Analysis by weighing, scoring and overlay are more popularly used today, especially 

related to the use of a Geographical Information System (GIS). Some government institutions 

such as Regional Development Planning Board, Public Work Office, Meteorological, 

Climatological, and Geophysical Agency often use this method to perform spatial analysis, 

especially related to natural disaster study. The methods used are easier and are compatible to 

use with varied parameter attributal data/variable that will be overlaid. However, this analysis 



             

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also has weaknesses because it greatly depends on the availability, data update and paramater 

data generalisation process to be used. Hence, it can decrease the accuracy of a flood 

vulnerability data output yielded. 

2.1 Sites 

This study was conducted in  Gajah Wong Sub Watershed of Yogyakarta County 

Province.  Gajah Wong Sub Watershed is a part of Opak Watershed through three areas of  

Regency/City and nine sub-districts. This study was performed for three months from 

January through March 2018. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 1. Map of  Gajah Wong sub watershed 



             

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2.2 Data Source 

Data used in this study were secondary data collected from some institutions such as  

Development Planning Agency at Sub-National Level (BAPPEDA) available to GIS portal 

(sig.jogjaprof.go.id), Mlati Climatology Station (BMKG) and Center for Water Resources 

Management (BPSDA). 

2.3 Data Processing 

This study used weighing and scoring method with overlay technique of attribute data 

of each flood vulnerability arrangement parameter. Data obtained were then adjusted, re-

classified and analysed to obtain final results in form of flood vulnerability map. Mapping of 

flood vulnerability needs some basic parameters/variables functioning as arrangement 

parameters. Flood vulnerability parameters in this study focuses on modified environmental 

vulnerability such as land use, slope inclination, rainfall, soil type, geology, location height 

and river buffer. Each of the parameters is rated for weighing and classification based on 

magnitude of effect on flood. The following are weighing and scoring of flood vulnerability 

parameter. 

2.3.1 Land Use  

Classification of land use is based on Indonesian National Standardisation Agency No. 

7645/2010 with modification based on scale of 1:50,000 – 1:25,000 (SNI, 2010: 13-28). 

Previously, these data were tested for accuracy using confuse matrix method. Land use is 

classified into five criteria with the highest score of 15 and lowest score of 3. 

Table 1. Weighing  and scoring of land use 

 

Land Use Value Weight Score 

Reservoir, River, Swamp, Open 

Land, Water Body, Pond, 

Freshwater Pond 

 

5 

 

 

 
 

 

 
 

 

3 

 

15 

 

Settlement, Industry, 

Mixed Plantation, Pier, Port, Field 

 

4 

 

12 

 

Agriculture, Irrigation Fields, 

Rainfed Rice Fields, yards, 

Airports 

 

3 

 

9 

 

Farm / Moor, Plantation, Shrub, 

Grass / Empty Land, Graveyard 

 

2 

 

6 

 

Production Forest, Protection 

Forest 

 

1 

 

3 



             

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2.3.2 Slope Inclination  

Classification of slope inclination is based on Social Forestry and River Flow Zone 

Management Development-General Director’s Decree No. P. 3/V-Set/2013 on River Flow 

Zone. Slope inclination is classified into five criteria, namely: flat, sloped, waved, steep, very 

steep. The highest score is 25 and lowest score is 5. 

Table 2. Weighing and scoring of slope inclination 

 

Slope Inclination (%) Criteria Value Weight Score 

0 – 8 Flat 5  

 

5 

25 

>8-15 Sloped 4 20 

>15-25 Waved 3 15 

>25-40 Steep 2 10 

>40 Very Steep 1 5 

 

 

2.3.3 Rainfall  

Data of rainfall are based on average rain/year for 14 years. Analysis used was inverse 

distance weighted (IDW) interpolation. Classification of rainfall are based on Social Forestry 

and River Flow Zone Management Development-General Director’s Decree No. P. 3/V-

Set/2013 on River Flow Zone Characteristic Identification Guidance. Data of rainfall are 

classified into 5 criteria, namely: low, rather low, medium, high and very high. Highest score 

is 20 and lowest score is 4. 

Table 3. Weighing and scoring of rainfall 

 

Rainfall (mm/year) Value Weight Score 

1500-2000 1 

4 

4 

>2000-2500 2 8 

>2500-3000 3 12 

>3000-3500 4 16 

>3500-4000 5 20 

 

2.3.4 Soil Type  

Classification of soil type is based on classification system issued by modified Farming 

Land Resource Research & Development-Big Hall Head (Subardja, Ritung, Anda, Suryani & 

Subandiono, 2014: 12-19). Soil type consists of Grumosol, Mediterranean, Latosol, Kambisol 

& Alluvial, Regosol. Highest score is 10 and lowest score is 2. 

 

 



             

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Table 4. Weighing and scoring of soil type 

 

Soil Type Value Weight Score 

Grumosol 5 

2 

10 

Mediterranean 4 8 

Latosol 3 6 

Kambisol /Alluvial 2 4 

Regosol 1 2 

 

2.3.5 Geology  

Rating of geology refers to Sigit and Paimin. Condition of geology consists of 

Andesite, Breccias, Limestone & sandstone, sediment rock mountain & deposit, 

conglomerate,  Colluvium, alluvial (Sigit, 2016; Paimin et al., 2010:). The highest score is 15 

and lowest score is 3. 

Table 5. Weighing and scoring of geology 

 

Geology Value Weight Score 

Andesite 5 

3 

15 

Breccias 4 12 

Limestone / Sandstone 3 9 

Sediment Rock Mountain & 

Deposit, Conglomerate, 
2 6 

Colluvium, Alluvial 1 3 

 

 

2.3.6 Location Height  

Location height is classified into 5 criteria, namely: low, rather low, medium, high, and 

very high (Ariyora et al., 2015). The highest score is 20 and lowest score is 4. 

Table 6. Weighing and scoring of location height 

 

Location Height (m) Value Weight Score 

< 12 5 

4 

20 

>12-50 3 12 

>50-75 2 8 

>75-100 1 4 

>100 1 4 

 

 

2.3.7 River Buffer 

River buffer is classified into three distances, namely: 25 meters, 50 meters and 100 

meters. The highest score is 25 and the lowest score is 5. 



             

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Table 7. Weighing and scoring of river buffer 

 

Distance (m) Criteria Value Weight Score 

0 – 25 Close 5 

5 

25 

>25-100 Medium 3 15 

>100-500 Far 1 5 

 

 

The collected data were then quantified in parameters of the highest and lowest score or 

in flood vulnerability variable, which would be used further to find interval scores to classify 

flood vulnerability levels. 

Table 8. Quantification of highest score and lowest score   

Variable Highest Score Lowest Score 

Land Use 15 3 

Slope Inclination 25 5 

Rainfall 20 4 

Soil Type 10 2 

Geology 15 3 

Location Height 20 4 

River Buffer 25 5 

Total 130 26 

 

Results of the highest score addition and lowest score of a flood vulnerability variable 

was thencalculated intervally as manuals of a flood vulnerability level classification. 

Vulnerability level was classified into three classes. Quantification of interval class 

classification was done by the following equation (Hermon, 2012:). 

 

I = (c-b)/k      (1) 

Where 

I = class interval distance, c = total highest score, b = total lowest score and k = 

desirable class. Desirable class is classified into three classes, namely, high, medium and low. 

Obtainable interval is 35. 

Table 9. Classification of flood vulnerability class 

Class Interval Vulnerability Score 

I High >96-130 

II Medium >61-96 

III Low 26-61 

 



             

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Processing of data used GIS software, Arcgis 10.3.1 with research flowchart (see the 

following flowchart) 

 

 

Figure 2. Flowchart of research 

 

 



             

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3. Results and Discussion 

3.1 Parameters of Flood Vulnerability  

Gajah Wong Sub Watershed consists of 13 types of land-uses dominated by residences, 

wet fields and yards. The wider portion of land-uses are residences and irrigated fields, 

namely, 3,562.75 ha and 1,000.56 ha. The lowest land-uses are lakes/dams and conserved 

forests, reaching less than 1% of total area of Gajah Wong Sub Watershed. 

The condition of slope inclination in Sub Gajah Wong Watershed was dominated by 

plain criteria with slope inclination interval of 0 – 8% having area of 4933.33 ha. Therefore, 

this conditionis categorised into high vulnerability because it becomes overflow target zone 

of rain water from highlands. Meanwhile, the sloped and waved criteria have very little 

portion, i.e. less than 0.1%.   

Data of rainfall were collected from Dadapan Gununganyar Observation Post, Ngaglik 

Observation Post, Sonayan Observation Post, Maguwoharjo observation post/Santan 

Observation Post, and SDA Potorono Observation Post, where these were distributed to some 

sub-districts. The results of interpolation indicated that distribution of average rainfall would 

tend to be low in direction of south and increase in northern direction leading to Merapi 

Mount. Dadapan Gununganyar Observation Post with averagely high rainfall reached 3,020 

mm/year. While, the lowest rainfall was recorded in Observation Post of SDA Potorono, 

reaching 1873 mm/year, distributed to southern parts of Gajah Wong Sub Watershed. 

Soil type in Gajah Wong Sub Watershed only consisted of Regosol and Kambisol. 

Entirely, Gajah Wong Sub Watershed was dominated by regosol soil type distributed to 

upstream zones of Merapi Mount zone directing to south, consisting of all Yogyakarta City 

zones and ending in downstream segment zones of  Gajah Wong Sub Watershed, including 

Pleret sub-district. While, Kambisol soil type was distributed to eastern and western zones of 

Sleman district, and covering Bantul district. 

Geological condition of Gajah Wong Sub Watershed was more dominated by rocks as a 

result of Volcano Mount deposits (Qmi), nearly reaching 99% of total area of Gajah Wong 

Sub Watershed, and less structured by results of Old Volcano Mount Deposits (Qmo) located 

near Merapi Mount. Gajah Wong Sub Watershed location height was sufficiently varied 

ranging from > 12 m² to > 100 m². The location height of more than 100 m² has the highest 

area reaching 61.94% of total area of Watershed. while height > 12 – 50 m² has the lowest 

area reaching 0.51%.  

Based on the results of river buffer analysis, 44.64% of the area has a distance from 

river of 500 m²,  9.12% area has 100 m² distance from the river.. While zonal areas consisting 



             

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of the distance of 25 m² from river has area of only 2.30%. Therefore, the farther the distance 

from river, thewider the coverage. 

 

Figure 3. Map of land-uses in Gajah Wong sub watershed 

 

 



             

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3.2 Flood Vulnerability  

All parameters of flood vulnerability were analysed by overlay to gain end scores 

aiming at determining vulnerability and distribution intervals. Flood vulnerability intervals 

are more dominated by medium, low and high intervals of flood vulnerability occupy smaller 

portion. Low vulnerability has area of 338.34 ha, reaching 6.86% and medium vulnerability 

has area of 4,595.62 ha, reaching 93.13% and high vulnerability only has 0.76 has, reaching 

0.02%. These cases were caused by data of ecologic vulnerability parameters of Gajah Wong 

Sub Watershed, having less varying scores and criteria, such as, data of slope inclination, soil 

type, geology and location height. Therefore, these were dominated by medium vulnerability 

criteria.  

 

Figure 4. Percentage of flood vulnerability intervals 

The results of analysis indicate that distribution of low flood vulnerability intervals 

seemed random, covering all administrative areas of Gajah Wong Sub Watershed, having 

smaller area than medium flood vulnerability intervals. Low flood vulnerability intervals 

were dominated by sub-district of Banguntapan which is the Ishoyet zone of IDW 

interpolation for rainfall data. The medium flood vulnerability intervals were more dominated 

by all zones of Gajah Wong Sub Watershed starting from upstream zone to downstream zone. 

Meanwhile, the high flood vulnerability intervals were distributed to Pleret sub-district being 

zone of river buffer analysis results in distance from 0 to 25 m² and in location height scope 

ranging from >12 to 50 m². 

 

 

 

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

Low Medium High



             

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The result of flood susceptibility mapping above is obtained from an overlay analysis of 

attribute data of flood susceptibility parameters. Those attribute data consist of land use, 

slope tilt, rainfall, soil type, geology, altitude, and river buffer. The utilisationof those 

parameters is in accordance with the provisions of the analysis of BNPB regulation no 12 of 

2012 which has been modified.  

Figure 5. Map of flood vulnerability in sub watershed gajah wong 

 



             

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In general, those analysis result shows that middle level of susceptibility is more 

dominating while the high level of susceptibility is only about the meeting area of the Gajah 

Wong River and Opak River. This condition is affected by the existence of Mount Merapi 

and the slope tilt so that rainwater runoff tends to dominate to the southward towards the 

downstream area. In this case, the flood tends to be dominated by submission flood. The 

middle and the downstream area are more impacted so that the water quality in the 

downstream is stained as the case in Umbulharjo sub-district which was marked with the 

increasing of E.Coli bacteria parameter (Winata & Haryanto, 2013). To date, the slums that 

are susceptible to flooding are still found along the Gajah Wong River because they live in 

the lowland and it is close to the river (Ropingi, 2004). The flood may hit the residential area 

around Papringan Village, Catur Tunggal.  

The rainfall area also shows the significant influence such as in the Banguntapan area 

which has a low average of rainfall. To date, based on the result of the observation, the 

government only issued the stroke policy in form of increasing and broadening embankment 

and also building the green open space to prevent the flood from hitting the residential area 

(Rachmawati & Budiarti, 2017). Other mitigation efforts had also been implemented even 

though it was not yet optimum. 

The preventive action is needed to decrease the futureimpacts. Flood mitigation and not 

– structurally (Heryanti & Kingma, 2012) through the organization empowerment and the 

local disaster communities such as Gajah Wong Care Community, Pokdarwis need to be 

maximised in terms of the budget and coordination (Ardiansyah, 2018) especially the people 

in the riverbank. Therefoe, people can adapt such as with the flood-like, the higher house, the 

boat owning, etc. (Thanvisitthpon, 2017). The synchronisation and synergy are needed by the 

local government policymakers because the true problem of flooding is  complex and 

involves various aspects of the environment, social, legal, economic and cultural (Kodoatie, 

2014).  

This study has some limitations, hence it is necessary to add and update data, such as 

data of rainfall that only consists of  rain average during 14 years, at least  20 to 30 years. 

Other disadvantages such as land use, slope of mountain, land type geology and height of 

location are data from 2016. Therefore, it should be updated to at least last year.  DEM data 

used to make a map of location height is only sourced from contour interpolation, this data 

should be obtained through interpolation of height point based on the results of a direct field 

survey. Analysis of river buffer has also a weakness related to a strong generalisation. 



             

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Analysis of  buffer should be a buffer of vulnerability map issued by related institutions, 

however, limited availability of data is one of the obstacles in this study. People residing in 

side of Gajah Wong River need understanding on mitigation and readiness to deal with flood. 

Flood occurring annually will result in physical damages. Physical development is flood 

resistance dam, groundsill and dam needs checking to anticipatethenext flood disaster. Care 

for keeping and maintaining rivers is also necessary to create clean and good river zone.  

Urban flooding can be effectively analysed using buffer analysis (Lyu et al., 2018). 

City centers are generally the areas with the highest flood hazard with the main parameters of 

land use (Abebe et al., 2018). Poverty level in an area affects flood vulnerability because it is 

related to the mindset and land ownership (Szewrański et al., 2018). The quality of buildings 

in urban areas affects flood vulnerability because it is associated with disaster risk reduction 

facilities in each house (Darabi et al., 2019). Flood vulnerability is related to the previous 

history of flooding in the nearest area and to consider the similarity of geographical 

characteristics of the region (Wang et al., 2019). This study differs from the previous ones in 

that it involves analyses relating to quarterly volcanic products and the influence of karst 

areas in the downstream watershed. The results of previous studies focused on land use, 

human behavior, and aspects of landform. 

This study can be used to analyse other risks, hazards, and mitigation of flood in Gajah 

Wong Sub Watershed, aiming at minimising the scales of the next damage results by 

implementing structural and non-structuralmitigation activities .  

4. Conclusion 

Flood vulnerability level in Gajah Wong Sub Watershed consisted of three intervals, 

namely: high, medium, and low flood vulnerability intervals. The high flood vulnerability 

interval covered an area of 0.76 ha (0.02%). The medium flood vulnerability interval covered 

an area of 4595.62 ha (93.13%). The low flood vulnerability interval covered an area of 

338.34 ha (6.86%). The medium flood vulnerability interval was more dominated by 

distribution to zone of Gajah Wong Sub Watershed. The low flood vulnerability interval 

dominated the sub-district of Banguntapan. The high flood vulnerability interval occupied 

smaller area distributed to Pleret sub-district which was an area of river buffer analysis in 

distance from 0 to 25 m².  

 

 



             

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Conflict of Interest 

The authors declare that there is no conflict of interest with any financial, personal, or 

other relationships with other people or organizations related to the material discussed in the 

article. 

References 

Abebe, Y., Kabir, G., & Tesfamariam, S. (2018). Assessing urban areas vulnerability to 

pluvial flooding using GIS applications and bayesian belief network model. Journal of 

Cleaner Production, 174, 1629-1641. doi:10.1016/j.jclepro.2017.11.066 

Ardiansyah, (2018) “Analisis Kerentanan dan Bahaya Banjir Sub Daerah Aliran Sungai 

Gajah Wong di Daerah Istimewa Yogyakarta dengan Sistem Informasi Geografis 

(SIG),” Universitas Negeri Yogyakarta, Thesis. Published 

Ariyora, Y., Budisusanto, Y. and Prasasti. I. (2015). Pemanfaatan Data Penginderaan Jauh 

dan SIG untuk Analisa Banjir (Studi Kasus: Banjir Provinsi DKI Jakarta). Geoid, 10, 

137–146. 

Beevers, L., Walker, G., and Strathie A. (2016). A-systems approach to flood vulnerability. 

Civil Engineering and Environmental Systems, 33, 1-15. 

BNPB. (2012). “Regulation of the Head of National Disaster Management Agency of the 

Republic of Indonesia Number 2 Year 2012 on General Guidance on Study of Disaster 

Risk.” BNPB, Jakarta, p. 62. 

Christiawan, P.I., and Wesnawa. I.G.A. (2014). Geografi Bencana. Yogyakarta: Graha Ilmu. 

Darabi, H., Choubin, B., Rahmati, O., Torabi Haghighi, A., Pradhan, B., & Kløve, B. (2019). 

Urban flood risk mapping using the GARP and QUEST models: A comparative study 

of machine learning techniques. Journal of Hydrology, 569, 142-154.  

Hartono, M.B. (2014). “Peran Masyarakat dan Pemerintah dalam Pengelolaan Air Limbah 

Domestik di Sub DAS Gajahwong,” Universitas Gadjah Mada, Thesis, unpublished.  

Hermon. D. (2012). Geografi Bencana Alam. Jakarta: Raja Grafindo Persada, 272. 

Heryanti, D.N., and Kingma, N.C. (2012). “Community Based Approach To Assess Flood 

Risk Perception Along Code River,” Indones. J. Geogr., vol. 44, no. 2, pp. 134–149. 

igović, L., Pamučar, D., Bajić, Z., & Drobnjak, S. (2017). Application of GIS-interval rough 

AHP methodology for flood hazard mapping in urban areas. Water (Switzerland), 9(6) 

doi:10.3390/w9060360 

Jia, B., Simonovic, S.P., Zhong, P., and Yu Z. (2016). A- Multi-Objective Best Compromise 

Decision Model for Real-Time Flood Mitigation Operations of Multi-Reservoir 

System. Water Resources Management, 30, 3363–3387. 

Kourgialas, N.N., and Karatzas, G.P. (2016). A flood risk decision making approach for 

Mediterranean tree crops using GIS; climate change effects and flood-tolerant species. 

Environmental Science and Policy, 63, 132–142. 

Lee, S., Kim, J. -., Jung, H. -., Lee, M. J., & Lee, S. (2017). Spatial prediction of flood 

susceptibility using random-forest and boosted-tree models in seoul metropolitan city, 



             

63 
 

A.Ardiansyah and Dyah Respati Suryo Sumunar / GEOSI Vol 5 No 1 (2020) 47-64 

 

korea. Geomatics, Natural Hazards and Risk, 8(2), 1185-1203. 

doi:10.1080/19475705.2017.1308971 

Lyu, H. -., Sun, W. -., Shen, S. -., & Arulrajah, A. (2018). Flood risk assessment in metro 

systems of mega-cities using a GIS-based modeling approach. Science of the Total 

Environment, 626, 1012-1025. doi:10.1016/j.scitotenv.2018.01.138 

Manfré, L.A. (2012) An Analysis of Geospatial Technologies for Risk and Natural Disaster 

Management. ISPRS International Journal of Geo-Information 1, 166–185. 

National Standardization Agency. (2010). Klasifikasi Penutup Lahan, Number. 7645:2010. 

Jakarta: BSN, 28. 

Paimin, P., Sukresno, S., and Purwanto, P. (2010). Sidik Cepat Degradasi Sub DAS. Bogor: 

Research and Development Center for Conservation and Rehabilitation, Bogor 

Forestry Research and Development Agency. 

Pettorelli, N., Laurance, W. F., O'Brien, T. G., Wegmann, M., Nagendra, H., & Turner, W. 

(2014). Satellite remote sensing for applied ecologists: Opportunities and 

challenges. Journal of Applied Ecology, 51(4), 839-848. doi:10.1111/1365-2664.12261 

Rachmawati R., and Budiarti C.V. (2017). “Use of Space and the Need for Planning in the 

Disaster-Prone Area of Code River, Yogyakarta, Indonesia,” Indones. J. Geogr., vol. 

48, no. 2, p. 178. 

Ramdhan, S., Arifin, H.S., Suharnoto, Y. (2018).“Towards Water Sensitive City: Lesson 

Learned From Bogor Flood Hazard in 2017,” International Conference On Energy, 

Environment And Information System, Semarang, Indonesia, vol. 31, pp. 1–5, 

February [E3S Web Conf. Indonesia, p. 05, 2017] 

Robert J. K. (2014). Rekayasa Dan Manajemen Banjir Kota. Yogyakarta: Andi Offset. 

Ropingi, R. (2004). “Perilaku Sosial Masyarakat Lembah Sungai Gajah Wong Yogyakarta,” 

J. Penelit. dan Eval. Pendidik., vol. 6, no. 1, pp. 57–71. 

Santillan, J.R., Marqueso, J.T., Makinano-Santillan, M. and Serviano, J.L. (2016). “Beyond 

flood hazard maps: Detailed flood characterization with remote sensing, gis and 2D 

modelling,” in International Archives of the Photogrammetry, Remote Sensing and 

Spatial Information Sciences - ISPRS Archives, 2016, vol. 42, no. 4W1, pp. 315–323. 

Sein, K.K., and Myint, T. (2016). Flood hazard mapping using hydraulic model and GIS : a 

case study in Mandalay city, Myanmar. Suan Sunandha Science and Technology 

Journal, 5, 15-20. 

Sigit, A. (2016). “Analisis Spasial Kemampuan Infiltrasi Sebagai Bagian Dari Indikasi 

Bencana Kekeringan Hidrologis di DAS Wedi, Kabupaten Klaten-Boyolali,” 

Proceedings of the 2016 National Geography Seminar UMS Disaster Risk Reduction 

Efforts Related to Climate Change, Surakarta, Indonesia, pp. 101–111. 

Stoica, A. and Iancu, I. (2011). Flood vulnerability assesment based on mathematical 

modeling. Mathematical Modeling in Civil Engineering, 1, 265–272. 

Subardja, D.S, Ritung, S., Anda M., Suryani E., and Subandiono R.E. (2014). Petunjuk 

Teknis Klasifikasi Tanah Nasional, 1st ed. Center for Agricultural Land Resources 

Research and Development Agency for Agricultural Research and Development, 45. 



             

64 
 

A.Ardiansyah and Dyah Respati Suryo Sumunar / GEOSI Vol 5 No 1 (2020) 47-64 

 

Suryantoro, A. (2009). Integrasi Aplikasi Sistem Informasi Geografis. Malang: Ombak, 214. 

Szewrański, S., Świąder, M., Kazak, J. K., Tokarczyk-Dorociak, K., & van Hoof, J. (2018). 

Socio-environmental vulnerability mapping for environmental and flood resilience 

assessment: The case of ageing and poverty in the city of wrocław, poland. Integrated 

Environmental Assessment and Management, 14(5), 592-597. doi:10.1002/ieam.4077 

Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2014). Flood susceptibility mapping using a 

novel ensemble weights-of-evidence and support vector machine models in 

GIS. Journal of Hydrology, 512, 332-343. doi:10.1016/j.jhydrol.2014.03.008 

Tehrany, M.S, Shabani, F., Neamah Jebur, M., Hong, H., Chen, W., & Xie, X. (2017). GIS-

based spatial prediction of flood prone areas using standalone frequency ratio, logistic 

regression, weight of evidence and their ensemble techniques. Geomatics, Natural 

Hazards and Risk, 8(2), 1538-1561. doi:10.1080/19475705.2017.1362038 

Thanvisitthpon, N. (2017). “Impacts of repetitive floods and satisfaction with flood relief 

efforts: A case study of the flood-prone districts in Thailand’s Ayutthaya province,” 

Clim. Risk Manag., vol. 18, pp. 15–20. 

Tika, M.P. (2005). Metode Penelitian Geografi. Jakarta: Bumi Aksara, 162. 

Wang, Y., Hong, H., Chen, W., Li, S., Pamučar, D., Gigović, L., . . . Duan, H. (2019). A 

hybrid GIS multi-criteria decision-making method for flood susceptibility mapping at 

shangyou, china. Remote Sensing, 11(1) doi:10.3390/rs11010062 

Weng, Q. (2010). Remote Sensing and GIS Integration. New York: The McGraw-Hill 

Companies. 

Winata, E., and Hartantyo, E. (2013). “Kualitas Air Tanah di Sepanjang Kali Wong Ditinjau 

dari Pola Sebaran Eschericjia Coli (Studi Kasus Kecamatan Umbulharjo),” J. Fis. Ilm., 

vol. XVII, no. Tabel 1, pp. 8–11.