Detection of Landslide-Prone Areas in Garut Regency Using

Composite Mapping Analysis and Geographic Information System

Ilham Badaruddin Mataburu

, Lia Kusumawati

and Ode Sofyan Hardi

Department of Geography, Universitas Negeri Jakarta, DKI Jakarta, Indonesia

Keywords: Landslide, CMA, GIS.

Abstract: Landslides represent one of the most frequent disasters in Garut Regency, resulting in various detrimental

impacts, including property damage, infrastructure impairment, and even loss of life. Identifying areas

susceptible to landslides can aid significantly in mitigation efforts. The objective of this study is to map these

landslide-prone regions within Garut Regency. The research employs a Composite Mapping Analysis (CMA)

model alongside Geographic Information System (GIS) technology, utilizing five parameters that influence

landslide occurrences: land use, slope, geology, rainfall, and elevation, in addition to one parameter for

comparing locations of previous landslides.The findings indicate that the CMA and GIS models, based on the

five parameters utilized, effectively contribute to understanding landslide occurrences, as evidenced by the

designated weight values of each parameter. Specifically, the geology, land use, and slope parameters

exhibited high weight values of 30.29, 29.84, and 17.71, respectively, highlighting their significant role in

landslide susceptibility in this region. The delineation of landslide-prone areas revealed that 9.99% of Garut

Regency is classified as having very high vulnerability, while 16.18% falls into the high vulnerability category,

predominantly located in the southern areas of the Cisompet, Pakenjeng, and Talegong sub-districts.

1 INTRODUCTION

Landslides are one of the most frequently occurring

natural disasters in Indonesia, often resulting in

significant loss of life and property. According to a

report by BNPB (2020), there were 726 landslide

incidents in 2019, leading to 114 fatalities and the

evacuation of 12.193 people due to the destruction of

homes. This marked an increase from the previous

year, which recorded 642 landslide events that also

resulted in casualties, injuries, and extensive damage

to residential buildings and critical infrastructure.

Garut Regency is classified as a high-risk disaster

area (BNPB, 2020), experiencing nine different types

of disasters frequently. From 2015 to 2019, 41

landslides occurred in this region resulting in six

fatalities and extensive damage to residential

buildings, requiring the evacuation of 433 people.

Landslides have also severely impacted critical

infrastructure, including roads and bridges, as well as

agricultural land. The high landslide risk in this

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region is driven not only by geophysical factors but

also by extensive land-use changes, as forest areas

have been rapidly converted into agricultural land and

residential areas over recent years.

Identifying landslide-prone areas is essential for

effective disaster management. Efforts to reduce

disaster impacts through prevention, mitigation, and

adaptation strategies must begin with an

understanding of landslide susceptibility based on the

types of landslides across different landforms.

Therefore, knowledge of landslide vulnerability is

crucial (Mubekti & Fauziah, 2008; Susanti &

Miardini, 2019).

The use of Geographic Information System (GIS)

technology facilitates the identification of disaster-

prone areas, and its application in mitigation efforts

has been extensively studied. Developing GIS models

integrated with statistical methods has proven highly

accurate in detecting landslide susceptibility (Pasang

& Kubicek, 2020). Integrating various landslide

causative factors helps clarify the role of each

Mataburu, I. B., Kusumawati, L. and Hardi, O. S.

Detection of Landslide-Prone Areas in Garut Regency Using Composite Mapping Analysis and Geographic Information System.

DOI: 10.5220/0013408900004654

In Proceedings of the 4th International Conference on Humanities Education, Law, and Social Science (ICHELS 2024), pages 331-339

ISBN: 978-989-758-752-8

331

indicator in influencing landslide potential (Mersha &

Meten, 2020). This approach is crucial for decision-

makers in formulating disaster-based spatial planning

for prevention and mitigation purposes. Therefore,

this study aims to map landslide-prone areas in Garut

Regency using the Composite Mapping Analysis

(CMA) model and GIS. This method is expected to

assess the contribution of each landslide-driving

factor both statistically and spatially

2 METHOD

2.1 Location

This research was conducted from May to September

2021 in Garut Regency, West Java Province.

According to the administrative division, the Garut

Regency consists of 42 subdistricts, 422 villages, and

21 urban villages.

2.2 Materials and Tools

The landslide-prone areas were analyzed using five

parameters in the form of spatial data (maps), namely

land use, slope, elevation, geology, and rainfall, as

well as landslide location points as comparison

parameters for determining weights and scores. The

analysis of landslide-prone areas was conducted

using Geographic Information System (GIS) software

with overlay functions. The tools used in this study

included GPS and ArcGIS 10.3 software.

2.3 Analysis Method

The landslide-prone areas were analyzed using the

Geographic Information System (GIS) overlay

method. The weighting and scoring for each

parameter affecting landslide susceptibility were

conducted using the Composite Mapping Analysis

(CMA) model. The weights and scores for each

parameter were determined by comparing the number

of observed landslide events with the expected

incidence of landslides in each region

(Boonyanuphap et al., 2001; Haryani et al., 2012).

The expected number of landslides represents the

total number of landslides that should ideally occur

within each area based on the area's total area. The

CMA mathematical formula for mapping landslide-

prone levels is as follows:

𝑇𝑅𝐿 =

∑

𝑊𝑖.𝑋𝑖







…………………(1)

Where:

• TRL is the landslide susceptibility level,

• Wi is the weight of parameter i, and

• Xi is the score for parameter i.

The weight Wi is formulated as:

𝑊𝑖 =



∑



……………………………(2)

Where:

• Mi is the average total observation for each

landslide susceptibility factor, and

• Xi is the score for each factor within each

parameter.

The score Xi is calculated as follow:

𝑋𝑖 =











∑

/



……………………..(3)

Where:

• Oi is the total observed landslide incidents,

and

• Ei ia the expected number of landslide

incidents.

The weight and score results from the CMA

model for each parameter were used as weights and

scores in the GIS analysis. All weights and scores

were then input as spatial data attributes for each

parameter. The overlay method was used to combine

all landslide-prone parameters, followed by

determining the susceptibility levels for each

landslide-prone area in the GIS environment.

Landslide susceptibility levels were divided into five

classes using the following equation:

𝐼=





………………........………..(4)

Where:

• I is the class interval distance,

• c is the highest score total,

• b is the lowest score total, and

• k is the desired number of classes.

3 RESULT

The paper size must be set to A4 (210x297 mm). The

document margins must be the following:

3.1 Score Values for Each Parameter

Each class within each parameter has a different

influence on landslide occurrence. The influence

value is measured through spatial comparison

between the distribution of landslide points and the

types and extents of each parameter, indicated by the

score value.

3.1.1 Land Use

Based on Table 1, the highest number of landslide

occurrences is found in dryland farming/garden land

use types. These areas are typically cultivated by the

ICHELS 2024 - The International Conference on Humanities Education, Law, and Social Science

332

Table 1. Area and Score of Land Use Parameter (Source: Data processing, 2021).

No. Land use Area (Ha) Area (%)

Number

landslide points

Score

1 Settlement 18.038,70 5.83 5 16.94

2 Building 1.09 0.00 0 0.00

3 Forest 63.714,43 20.58 1 1.12

4 Man

rove forest 32.09 0.01 0 0.00

5 Fish

17,31 0.01 0 0.00

6 Grassland 2.736,84 0.88 0 0.00

7 Waterbod

1.701,18 0.55 0 0.00

8 Garden 51.154,24 16.52 16 22.42

9 Irri

ated rice fiel

7.285,86 5.58 3 12.44

10 Rain-fed rice fiel

40.353,45 13.03 10 17.76

11 Shrubland 56.312,02 18.19 12 15.27

12 Dry fiel

57.981,82 18.73 9 11.12

13 Sand embankment 270.40 0.09 0

Total 309.599,43 100.00 56 97.07

Table 2. Area and Score of Elevation Parameter (Source: Data processing, 2021)

No. Elevation Area (Ha) Area (%)

Number landslide

points

Score

1 <500 82665,08 26.70 19 34.06

2 500 - 1000 120470,95 38.91 20 24.60

3 1000 - 1500 79680,82 25.74 15 27.89

4 1500 - 2000 22037,01 7.12 2 13.45

5 >2000 4745,57 1.53 0 0.00

Jumlah 309599,43 100.00 56 100.00

community as horticultural gardens with seasonal

crops. The root systems of these crops are not strong

enough to bind the soil effectively. Similarly, rain-fed

rice fields, which have shallow roots, also contribute

to soil stability issues. These two types of land use

have the highest scores in terms of their contribution

to landslide occurrences in the Garut Regency. Other

land use types with relatively high scores include

shrubland and dry fields (see Table 1). The

distribution of land use types and landslide points can

be seen in Figure 2a.

3.1.2 Elevation

The highest number of landslide occurrences is found

at elevations of 500–1000 meters above sea level (m

asl) and below 500 m asl. Generally, landslides rarely

occur at elevations above 1500 m asl. This is related

to land interventions from human activities, which are

typically conducted at elevations below 1500 m asl.

Areas at elevations above 1500 m are seldom utilized.

The highest scores based on spatial comparisons are

found at elevations <500 m asl and at elevations of

1000–1500 m asl (see Table 2). These areas are

generally located in the southern part and represent

the lowest areas within the study region. The spatial

distribution of elevation classes and landslide points

is presented in Figure 2b.

3.1.3

Rainfall

Rainfall plays an important role in landslide

occurrences (Tajudin et al., 2018). The infiltration of

water into the soil increases the soil mass and creates

a friction plane on the bedrock, making it more

slippery and causing the soil to become more prone

to movement (Paimin et al., 2009). The rainfall data

used in this study is the maximum daily rainfall,

divided into two classes. Based on the spatial

comparison calculations, both rainfall classes show

close score values (see Table 3). The higher score is

found in the rainfall class of 13.6–20.7 mm, with a

score of 50.25. This value does not differ significantly

from the score in the 20.7–27.7 mm class, which is

49.75. The distribution of rainfall classes and

landslide points is presented in Figure 2c.

Detection of Landslide-Prone Areas in Garut Regency Using Composite Mapping Analysis and Geographic Information System

333

Table 3. Area and Score of Rainfall Parameter (Source: Data processing, 2021)

No. Rainfall (mm/hr) Area (Ha) Area (%)

Number landslide

points

Score

1 13.6

–

20.7 192.768,91 62.26 35 50.25

2 20.7

–

27.7 116.830,52 37.74 21 49.75

Total 309.599,43 100.00 56 100.00

Table 4. Area and Score of Slope Parameter (Source: Data processing, 2021)

No. Slope Area (Ha) Area (%) Number landslide points Score

1 0 - 8 % 50.209,94 16.2 1 2.41

2 8 - 15 % 51.824,67 16.74 3 6.99

3 15 - 25 % 70.362,27 22.73 15 25.75

4 25 - 40 % 73.192,59 23.64 21 34.66

5 >40% 64.009,96 20.68 16 30.19

Total 309.599,43 100.00 56 100.00

3.1.4 Slope

Based on the slope, the study area is mostly

mountainous, with slopes greater than 15%. The

central to northern areas, as well as the southern

coastal area, are narrow plains with slopes of less than

15%. Landslide occurrences are found on slopes

greater than 15%, with the highest number of

occurrences in the 25–40% slope class. Based on

spatial comparison, the highest scores are in the 25–

40% and >40% slope classes. Score comparisons

show that the highest scores are in areas with slope

classes of 25–40% and >40%, with 21 and 16

landslide points, respectively. Subsequently, the

score decreases with decreasing slope class. The

score values and slope class distribution are presented

in Table 4 and Figure 2d.

3.1.5 Geology

Geology is related to soil stability in terms of

landslide potential; geological formations that are less

compact are more likely to detach and experience soil

movement, while more compact rocks tend to be

more resistant to soil movement which can lead to

landslides. Generally, the potential for soil movement

is associated with the properties of rocks in

responding to water or vibrations as landslide triggers

(Paimin et al., 2009). Geologically, the study area is

divided into 30 types of geological formations, most

of which are volcanic in origin, consisting of volcanic

rocks. The study area is dominated by tuff and breccia

rocks, pyroclastic deposits, and young volcanic rocks,

which are primarily found in the central to southern

parts of the study area (see Figure 2e). The score

values from the CMA analysis through spatial

comparisons between landslide points and geological

types indicate that out of 30 geological types in the

study area, only 7 contain landslide points with

varying numbers. The highest scores are found in reef

limestone and tuff and breccia members, with values

of 25.46 and 21.14, respectively. Other geological

types with relatively high scores include young

volcanic rocks, old volcanic breccia, and pyroclastic

deposits (see Table 5).

3.2 Weight of Each Parameter

Calculations using the CMA and SIG models,

according to Equation 2, yield the weight values for

each parameter in relation to its correlation with the

distribution of landslide occurrences. The weight of

each parameter correlates with the contribution of

each parameter to landslide occurrences, and the

results indicate that geology, land use, and slope

gradient parameters exhibit the highest weight values.

3.2.1 Delineation of Landslide Hazard Areas

Landslide hazard levels were calculated using GIS by

applying Equation 1 across all parameters, including

land use, slope, rainfall, elevation, and geology. The

analysis was conducted through overlay processes

and mathematical operations on the weight and score

values according to the equation below:

TRL = 29.84 SPL + 17.71 SLE + 7.73 SCH + 30.29

SGE + 14.43 SEle

Where:

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Table 5. Area and Score of Geology Parameter (Source: Data processing, 2021)

No. Geology type Area (Ha) Area (%)

Number of

landslide

oints

Score

1 Breccia from old volcanic rocks 23.377,73 7.55 5 15.10

2 Lava from old volcanic rocks 588,56 0.19 0 0.00

3 Breccia from old volcanic products 1.175,29 0.38 0 0.00

4 Unaltered old volcanic products 16.10 0.01 0 0.00

5 Mandalawangi-Mandalagi volcanic formation 1.693,12 0.55 0 0.00

6 Pumice tuff and breccie 2.228,47 0.72 0 0.00

7 Sangianganjung volcanic rocks 156,11 0.05 0 0.00

8 Guntur-Pangkalan volcanic rocks 12.648,52 4.09 1 5.58

9 Young volcanic rocks 61.388,69 19.83 15 17.25

10 Loose recent sediments 16.918,58 5.46 0 0.00

11 Lava guntur 3.578,22 1.16 0 0.00

12 Kolovial 102,83 0.03 0 0.00

13 Olf volcanic efflata deposits 2.019,79 0.65 0 0.00

14 Alluvial 8.311,41 2.68 0 0.00

15 Old volcanic products 7.239,49 2.34 0 0.00

16 Papandayan volcanic efflata 3.821,86 1.23 0 0.00

17 Waringin-Bedil Andesite 6.064,75 1.96 0 0.00

18 Intrusive rocks 3.461,63 1.12 0 0.00

19 Koleberes formation 1.881,78 0.61 0 0.00

20 Pyroxene andesite 88,67 0.03 0 0.00

21 Pyroclastic deposits 45.556,77 14.71 7 10.85

22 Tuff and breccia members 86.839,23 28.05 26 21.14

23 Benteng formation 15.308,18 4.94 1 4.61

24 Lower benteng formation 61,21 0.02 0 0.00

25 Quartz diorite 116,87 0.04 0 0.00

26 Jampang formation 1.788,07 0.58 0 0.00

27 Alluvial dan coastal deposits 149.04 0.05 0 0.00

28 Andesite 197.62 0.06 0 0.00

29 Andesite hornblende 47.68 0.02 0 0.00

30 Coral reef limestone 2.773,14 0.90 1 25.46

Total 309.599,43 100.00 56 100.00

• TRL represents the landslide hazard level,

• SPL is the score for the land use parameter,

• SLE is the score for the slope parameter,

• SCH is the score for the rainfall parameter,

• SGE is the score for the geology parameter,

and

• SEle is the score for the elevation parameter.

The calculation results showed a minimum value of

431,11 and a maximum value of 2.844, 91.

Subsequently, the landslide hazard levels are divided

into 5 classes following Equation 5 as follows:

Detection of Landslide-Prone Areas in Garut Regency Using Composite Mapping Analysis and Geographic Information System

335

Figure 2: Map of (a) Land use; (b) Elevation; (c) Rainfall; (d) Slope; (e) Geology.

Table 6. Weight of Landslide Parameter

No Parameter Weight

1 Land use 29.84

2 Slope 17.71

3 Rainfall 7.73

4 Geology 30.29

5 Elevation 14.43

Total 100.00

Source: Data processing, 2021

Table 7. Landslide Hazard Class (Source: Data analysis,

2021)

Landslide Hazard Class TRL Value

Very low < 913.87

Low 913.87 - 1396,63

Moderate 1396,63 - 1879,39

h 1879,39 - 2362,15

Ver

h > 2362,15

According to Table 7, areas with a very low landslide

hazard level constitute the largest area in the Garut

Regency, while areas with a very high hazard level

represent the smallest area (9.99%). These high-

hazard areas are generally distributed from the

southern to central parts and on the left side near the

border between Garut and Tasikmalaya Regencies.

This region consists of southern limestone hills and

areas surrounding the central and eastern volcanic

mountains in Garut Regency. It typically features

steep slopes and high rainfall, especially in

mountainous areas approaching borders with

neighboring regencies (see Figure 3).

The largest extent of very high hazard areas is

primarily found in Cisompet District, with significant

areas also in Cikelet District, Pakenjeng District, and

partially in Bungbulang and Banjarwangi Districts.

Similarly, high-hazard areas are concentrated in

Cisompet and Pakenjeng, which have the largest land

area in this category.

3.3 Discussion

The use of CMA and GIS models in landslide hazard

mapping, employing five determining parameters and

comparing them with landslide occurrence data,

effectively identifies areas with similar

characteristics across the five parameters and assesses

their hazard levels both statistically and spatially. The

application of GIS and the CMA model indicates that

landslide occurrences in Garut Regency are

influenced by specific factors: geological types of tuff

and breccia, land use for plantations, slopes between

5–40% and greater than 40%, elevations of 500–1000

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336

Table 8. Landslide Hazard Area in Garut Regency (Source: Data analysis, 2021)

No Landslide Hazard Level Area (ha) Area (%)

1 Not hazardous 130.145,85 42.04

2 Slightly hazardous 52.132,34 16.84

3 Moderately hazardous 46.295,13 14.95

4 Hazardous 50.094,38 16.18

5 Very hazardous 30.931,73 9.99

Total 309.599,43 100.00

Figure 3: Map of Landslide Hazardous in Garut Regency.

meters, and a generally even distribution concerning

rainfall.

Geology and land use are parameters closely

associated with landslide occurrences (Faizana et al.,

2015; Pasang & Kubicek, 2020). Research findings

indicate that these two parameters have the highest

weights based on landslide distribution in Garut

Regency. Landslide distribution is concentrated in

areas with geological types such as tuff and breccia

and young volcanic rocks, both of which formed from

past volcanic activity, particularly in the central to

northern regions (Sulaksana et al., 2014). The

southern part consists of karst hills, featuring varied

topography with steep to very steep slopes. Rock

layers that allow water to seep until reaching

Detection of Landslide-Prone Areas in Garut Regency Using Composite Mapping Analysis and Geographic Information System

337

impermeable layers can trigger slippage, leading to

landslides (Paimin et al., 2009).

Another determining factor for landslide occurrences

in the study area is land use, which relates to the land's

ability to respond to water. Landslides commonly

occur on agricultural lands, such as fields and dryland

farms. The incidence of landslides increases with the

rapid change in land use in this region, as Garut

Regency is among the areas with a high rate of land-

use change, especially around slopes and the foothills

of volcanic mountains, which are fertile for

horticulture or seasonal crops like corn, cabbage,

scallions, and other vegetables (Muldiana et al.,

2016). Intensive land-use changes, driven by

infrastructure development or agricultural expansion,

can increase landslide potential (Moresi et al., 2020;

Pasang & Kubicek, 2020). Agricultural activities in

Garut Regency occur on various topographies, with

dryland farming spread across hills and the foothills

to the slopes of mountains, including areas with

slopes > 25%. This situation disrupts soil stability and

potentially increases landslide occurrences

(Anbalagan et al., 2015). The type of land use affects

slope stability, as seasonal agricultural lands with

shallow-rooted crops decrease slope stability,

increasing the risk of landslides (Susanti & Miardini,

2019). Landslide events in Garut Regency primarily

occur on slopes > 25%, with land uses such as

plantations, fields, and shrublands.

4 CONCLUSIONS

The study produced several conclusions:

1. The use of GIS and the CMA model

effectively maps landslide hazard areas by

comparing five determining parameters with

the landslide occurrence parameter through

weighting and scoring of each parameter.

2. Geological factors, land use, and slope are

the parameters with the highest contribution

to landslide occurrences in Garut Regency.

3. The distribution of high and very high

landslide hazard areas is generally located in

the southern to central parts of Garut

Regency, which consist of a series of hills

and mountains, primarily in Cisompet and

Pakenjen Districts. Areas classified as

relatively non-hazardous to slightly

hazardous are typically found in the central

to northern and western regions, which have

gentler slopes, including Garut Kota,

Tarogong Kidul, Tarogong Kaler, and

Banyuresmi Districts.

RECOMMENDATIONS

The resulting landslide hazard map can serve as a

consideration in spatial planning as an initial step to

determine the type and location of land use allocation

as part of landslide disaster mitigation efforts in the

Garut Regency. For future research, the use of the

CMA model and GIS relies heavily on the number

and types of parameters applied. Additional

parameters should be included to obtain improved

results.

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