ISSN: 0852-0682.
EISSN: 2460-3945
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Evidence-Based Landslide Hazard Mapping in Purworejo using the Information Value Model Sudaryatno.
Prima Widayani.
Totok Wahyu Wibowo *.
Bagus Wiratmoko, and Wahyu Nurbandi Department of Geographic Information Science.
Faculty of Geography.
Universitas Gadjah Mada.
Jl.
Kaliurang Km.
5 Bulaksumur.
Sleman.
Yogyakarta 55281.
Indonesia Corresponding Author .
-mail: totok.
wahyu@ugm.
Received: 16 January 2019 / Accepted: 8 May 2019 / Published: 1 August 2019 Abstract.
Purworejo District, which is located in Central Java.
Indonesia, is prone to landslides.
These are a natural hazard that often occur in mountainous areas, so landslide hazard analysis is needed to develop mitigation strategies.
This paper elaborates on the use of an evidence-based statistical approach using the Information Value Model (IVM) to conduct landslide hazard The parameters of slope, aspect, elevation, rainfall.
NDVI, distance from rivers, distance from the road network, and distance from faults were employed for the analysis, which was conducted based on a raster data environment, since the pixel is the most appropriate means to represent continuous data.
Landslide evidence data were collected by combining secondary data and interpreting satellite imagery to identify old landslides.
The IVM was successfully calculated by combining factors related to disposition to landslides and data on 19 landslide occurrences.
The results helped produce a landslide susceptibility map for the northern and eastern parts of Purworejo District.
Keywords: landslide, hazard.
Information Value Model.
Abstrak.
Kabupaten Purworejo adalah salah satu daerah rawan longsor di Indonesia.
Tanah longsor adalah bencana alam yang sering terjadi di daerah perbukitan atau pegunungan.
Analisis spasial untuk pemetaan bahaya bencana alam diperlukan untuk mitigasi yang tepat.
Penelitian ini menggunakan Information Value Model (IVM) berbasis bukti untuk melakukan pemetaan bahaya tanah longsor.
Ada delapan parameter yang digunakan dalam penelitian ini, yaitu kemiringan lereng, arah hadap lereng, elevasi, curah hujan.
Normalized Difference Vegetation Index (NDVI), jarak dari sungai, jarak dari jaringan jalan, dan jarak dari patahan.
Analisis dilakukan menggunakan data raster karena piksel adalah cara yang paling tepat untuk merepresentasikan data kontinyu.
Data kejadian longsor dikumpulkan dengan menggabungkan data sekunder dan PHQJLQWHUSUHWDVLNDQ FLWUD VDWHOLW XQWXN PHQJLGHQWLyANDVL ORQJVRU ODPD ,90 EHUKDVLO GLKLWXQJ dengan menggabungkan faktor-faktor predisposisi tanah longsor dan 19 data kejadian tanah Kalkulasi IVM tersebut menghasilkan peta kerentanan longsor di bagian utara dan timur Kabupaten Purworejo.
Kata kunci: tanah longsor, bahaya.
Information Value Model.
Introduction Landslides are a natural hazard that can cause casualties, property loss and environmental damage, and which occur mostly in mountainous areas (Devkota et , 2.
Although categorized as a natural hazard, landslides are not only a natural phenomenon, but are also caused by human land-based activities.
They are one of the many natural hazards that frequently occur in Indonesia, especially in Purworejo.
Central Java (Hadmoko et al.
, 2017.
Based on data provided by the National Agency for Disaster Management (BNPB), there were 97 landslide occurrences in Purworejo in the period 2000 to 2017, which resulted in many casualties.
Evidence-Best Landslide.
(Sudaryatno et al.
Spatial analysis can be used as a landslide hazard mitigation strategy (BNPB, 2.
As disaster knowledge develops into a Disaster Risk Reduction (DRR) paradigm, geospatial data is urgently needed (Kerle et al.
, 2.
Generally.
DRR is divided into four stages, and each stage needs such data (GTZ, 2.
Hazard maps are essential to DRR, since knowledge of a hazard zone can be used to determine the mitigation process.
Furthermore, many risk transfer scenarios could be implemented by conducting analysis based on the risk map which results from the hazard and vulnerability maps.
Landslide hazard mapping has been subject to long and strenuous efforts by previous researchers, with various methods applied (Hadmoko et al.
, 2010.
Nugraha et al.
, 2015,
Saputra et al.
, 2.
A new method for landslide hazard mapping, called Information Value Model, has recently been proposed by Sharma et al.
, although it has not been widely implemented in Indonesia yet.
The results show that the Information Value Model could produce accuracy of up to 81.
8% (Ba et al.
, 2.
It is a data-driven method and can be applied to assess landslide susceptibility.
Therefore, there is a possibility to implement the new method in Purworejo.
The rapid advances in geospatial technology have provided many opportunities in the mapping for example, the ubiquitous remote sensing nowadays could provide an opportunity to explore the world on an appropriate scale.
Moreover, the Geographic Information System (GIS) has also been extensively used in geospatial Both technologies can provide great data and analysis capability in DRR studies, since natural disasters have geospatial properties.
Study Area Purworejo is a district located in the southern part of Java Island, about 65 kilometers west of Yogyakarta City (Figure .
The area is 1.
034 km2 and consists of 16 sub-districts.
The northern and eastern areas are dominated by mountains, where landslides frequently occur.
The southern area is GRPLQDWHGE\yAXYLDOSODQHVDQGLVPRVWO\XVHG DVSDGG\yAHOGV3XUZRUHMRKDVDWURSLFDOFOLPDWH with an annual average temperature ranging from 19AC to 28AC, and humidity of 80% to 90%.
The highest rainfall usually occurs in December, up to 311 mm/month, which corresponds to the occurrence of landslides.
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EISSN: 2460-3945
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Evidence-Best Landslide.
(Sudaryatno et al.
Research Method Landslide Data Inventory Previous landslide data were provided by the Regional Disaster Management Agency (BPBD) and satellite imagery.
Landsat 8 imagery was used to identify old landslides based on visual interpretation, and data from BPBD were used as an initial indicator of old landslide locations.
Table 1 shows the total number of landslide occurrences in Purworejo by sub-district.
Fieldwork was conducted in some landslide locations to assess the accuracy and reliability of the A drone survey was also conducted to capture samples of the current condition of the landslide area.
Table 1/DQGVOLGHRFFXUUHQFHVLQ3XUZRUHMR'LVWULFW Number of Landslides Subdistrict
Total
Bagelen
Banyuurip
Bayan
Bener Bruno
Butuh Gebang
Grabag
Kaligesing Kemiri Kutoarjo
Loano
Ngombol
Pituruh
Purwodadi Purworejo
Total
6RXUFH33,'3XUZRUHMR'LVWULFW
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Evidence-Best Landslide.
(Sudaryatno et al.
Predisposition to Landslide Factors In this research, we used eight factors related to predisposition to landslides to construct the landslide susceptibility model.
These were slope, aspect, elevation, rainfall.
Normalized Difference Vegetation Index (NDVI), distance from rivers, distance from road network, and distance from faults.
All the data were prepared in the raster format, as the analysis was raster based.
Elevation strongly correlates with physical aspects such as rainfall, hydrologic features, geology and soil.
These physical aspects combine with slope gradient, which plays an important role in landslide hazard occurrence.
Elevation, slope and aspect were derived from Shuttle Radar Topographic Mission (SRTM) imagery, which has a spatial resolution of 30 High rainfall intensity can be considered as a landslide triggering factor.
Monthly cumulative rainfall data were used, since these represent annual rainfall intensity.
Distance from the hydrographic network, faults and road network were added, as suggested by Ba et .
Theoretically, dense vegetation may stabilize slopes during intense precipitation.
this research, vegetation density is represented by NDVI values based on Landsat 8 imagery.
Figure 2 shows the methods employed to obtain the factors related to predisposition to Information Value Model (IVM) The IVM was developed from information theory to perform statistical analysis.
Based on this model, the information value of landslide predisposition factors will be used for the landslide modelling.
The formula follows the expression of the information value I.
H) of each landslide predisposition factor xi (I = 1,2,3,A.
In this case.
H denotes the possibility of landslide occurrence.
S is the total of mapping units, and N is the total area of landslides in the research area.
Si is the number of mapping units with the presence of a predisposing factor xi, while Ni is the total area of landslides with the presence of a predisposing factor xi.
The total information (I) of each mapping unit can be calculated by adding together )LJXUH/DQGVOLGHSUHGLVSRVLWLRQIDFWRUH[WUDFWLRQPHWKRGV ISSN: 0852-0682.
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Evidence-Best Landslide.
(Sudaryatno et al.
Figure 3 shows some examples of landslide 7KH LGHQWLyAHG ODQGVOLGHV ZHUH WKHQ processed using IVM (Formula .
Nine landslide predisposition factors were used to calculate the IVM value.
Table 2 shows the results of the IVM calculation.
Landslides are often referred to as slope instability, which is caused by geomorphological factors, especially slope angles (Aman et al.
, 2.
Generally, a steep slope is more susceptible to Results and Discussion ,GHQWLyAFDWLRQ RI ODQGVOLGH HYHQWV WKDW ODQGVOLGHVEHFDXVHWKHRYHUODQGyARZYHORFLW\ have occurred is challenging, since over time is high, and able to move soil material to a the location experiences changes such as lower area due to gravity.
The SRTM image The reference data in Table 1 show that the southern part of Purworejo show a large number of landslide events, but Regency has an altitude of <100 meters above the data is not completed by other attributes sea level .
, with a gentle slope of <5A.
such as location or area to facilitate the Meanwhile, the northern area is mountainous.
LGHQWLyAFDWLRQ RZHYHUODQGVOLGHORFDWLRQV with an elevation of more than 400 m asl and ZHUH VXFFHVVIXOO\ LGHQWLyAHG XVLQJ /DQGVDW a steep slope between 10-35A (Figures 4a and imagery, most of which were recent events.
the information values of all predisposing The resulting value may be positive or negative, with the following class: if I < 0, the possibility of landslide is lower than average.
if I = 0, the possibility of landslide is equal to the average.
and if I > 0, the possibility of landslide is higher than average (Ba et al.
The higher the information value, the higher the probability of landslide occurrence.
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Evidence-Best Landslide.
(Sudaryatno et al.
Table 2.
,QIRUPDWLRQ9DOXH0RGHOFDOFXODWLRQ
Factor Elevation Class Distance from river Distance from fault Rainfall .
m/mont.
Distance from road network ISSN: 0852-0682.
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IVM
110,700
139,058
57,600
38,700
126,226
11,700
>400
176,476
19,800
607,228
9,000
165,254
46,800
1,205,224
132,843
116,100
138,556
62,100
>35
10,946
4,500
1,205,223
283,239
238,500
238,500
Flat
11,048
North
125,031
29,700
Northeast
113,211
30,600
Southeast
136,562
1,205,224
158,404
236,204
36,900
32,400
South
174,603
32,400
Southwest
175,078
20,700
West
168,312
32,400
Northwest
142,975
30,600
<1000
1,010,764
161,100
145,442
77,400
9,221
>4000
<600
83,205
49,500
84,245
1,205,385
39,958
98,100
76,549
4,500
>2400
883,737
1,205,836
78,100
236,204
236,204
<30
4,257
48,573
4,500
58,500
1,205,212
250,449
236,204
782,993
87,300
>120
118,940
88,200
<200
806,890
195,300
269,660
9,900
4,500
1,205,887
86,113
236,204
28,689
21,600
>800
14,535
7,200
<0,55
249,048
69,300
11,700
31,500
0,55-0,65
NDVI
Ni .
630,621 East Aspect N .
<100 Slope
0,65-0,75
96,985
1,207,274
183,102
236,204
0,75-0,85
528,412
105,300
>0,85
149,727
20,700
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Evidence-Best Landslide.
(Sudaryatno et al.
Figure 4 .
elevation and .
slope of research area.
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Evidence-Best Landslide.
(Sudaryatno et al.
The slope map shows that the frequency of landslide events in Purworejo increases with slopes up to 35A and then decreases with steeper slopes greater than 35A.
In the highest slope class, i.
e greater than 35A, the majority of landuse is shrubs, which have less weight than wood or water.
This could be the reason why the steepest slope class has a lowest number of events.
Moreover, the areas of the steepest slopes are also the smallest.
The highest IVM value can be found in the fourth class of slope .
A - 35A), which has a value of 0.
The positive value means that it has high landslide With regard to elevation, the number of landslide points at elevations of <100, 100-200, 200-300, 300-400 and > 400 are 122, 64, 45, 13, 21 respectively.
The incidence of landslides decreases at an altitude of 300 meters to more than 400 meters.
Surprisingly, a higher elevation does not imply a more susceptible area.
This fact is supported by the negative IVM value for the top two elevation a negative IVM value indicates that the level of landslide susceptibility is very low.
The domination of andesite rocks that may have not experienced intensive weathering in high elevation areas in Purworejo gives a plausible explanation of why the possibility of ODQGVOLGHVLVORZWKHUH2YHUDOOWKHGLVWULEXWLRQ of landslide points in Purworejo Regency is dominated by slopes of 20A-35A, which are distributed on the north and east sides and dominated by mountainous areas.
The landslide event points in Purworejo are dominated by slopes facing north, northeast and east, with the number of occurrences 33, 46 and 36 respectively.
The IVM calculations show a weight range of -0.
50 to 0.
The highest values are for east and northeast facing slopes.
Slopes that face south tend to have longer sun exposure, so the air temperature is higher, which increases erosion (Rajakumar et al.
Quan & Lee, 2.
The IVM results from this study area do not completely agree with SUHYLRXV yAQGLQJV VLQFH PRVW RI WKH ODQGVOLGH data refer to the western part of Menoreh Hill.
Aspect map can be found in Figure 5a.
The aspect of a slope determines the ISSN: 0852-0682.
EISSN: 2460-3945 duration of sunlight on it.
This is considered to be one of the landslide predisposition factors because it affects soil moisture, vegetation cover and soil strength (Hadmoko et al.
, 2017.
If a slope faces the sunAos radiation for a long period of time, there will be a high possibility of more vegetation cover as there will be more light for photosynthesis.
Based on this fact, it is assumed that the denser the vegetation, the lower the possibility of a landslide occurring.
It is important to note that this assumption is an independent point of view with regard to landslide predisposition factors.
Figure 5a shows the slope aspect in Purworejo district, which is dominated by southeast, south and southwest facing slopes.
Areas with dense vegetation cover tend to be able to withstand soil loads, even though some landslide events are caused precisely by the weight of the surface objects, including vegetation.
The vegetation data are represented by NDVI, which is conceptually related to vegetation density (Figure 5.
Low-density vegetation with an NDVI value of less than 0.
55 has a high level of landslide susceptibility.
However, that with a high-density value of 0.
75 to 0.
85 also has a high level of landslide susceptibility, with an IVM value of 0.
Rain is the main driving factor for landslides because it can directly or indirectly reduce the strength of the rock to resist soil loads (Hadmoko et al.
, 2017.
The higher the rainfall, the higher the potential for landslides.
Areas that have more than 120 mm/month rainfall have a high level of landslide susceptibility, with an IVM value of 1.
A rainfall map of the research area is presented in Figure 6a.
The distance from faults, rivers and roads affects the level of landslide susceptibility (Figure 6 and Figure .
However, the IVM value indicates a nonlinear relationship due to the limitations of the landslide data.
From the calculations, it was found that the area 1200-1800 meters from a fault had the highest IVM value .
, followed by a distance shorter than 600 meters .
There should be a strong correlation between faults and landslides, as suggested by Gokceoglu and )RUXP*HRJUDA9RO -XO\ Evidence-Best Landslide.
(Sudaryatno et al.
Aksoy .
The high IVM value of distances shorter than 600 meters from faults supports previous research.
In addition, the highest IVM value based on distance from faults in this study area gives a new perspective that not all the landslides which have occurred in the study area have been the result of the existence RI IDXOWV 2WKHU GLVWDQFH FODVVHV KDYH D ORZHU level of landslide susceptibility.
The data related to distance from roads show that the lowest level of susceptibility is an area that has a distance of 200-600 meters from a road.
This has a negative IVM value, while other distance classes have a positive value.
Regions that are 1000-2000 meters distant from a river have the highest vulnerability value, at 999068, whereas areas that are under 1000 meters from a river have a lower landslide susceptibility level, with an IVM value of Many researchers have used distance from roads as a factor in landslide hazard assessment because the natural conditions of a slope might be damaged by road construction (Wu et al.
, 2.
Figure 5 .
aspect map and .
NDVI results of research area.
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Evidence-Best Landslide.
(Sudaryatno et al.
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Evidence-Best Landslide.
(Sudaryatno et al.
Figure 7 .
distance from road network and .
distance from faults.
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Evidence-Best Landslide.
(Sudaryatno et al.
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Figure 8 shows the landslide susceptibility map using the IVM method.
The eastern side of Purworejo is dominated by a very high level of susceptibility.
the northwest is dominated by moderate to high levels.
while the south is dominated by low levels of landslide The level of susceptibility is categorized based on the total IVM value This total is the sum of the IVM of each parameter without a weighting multiplication The lowest total IVM value is -7.
5 while the highest is 6.
The higher the IVM value, the higher the level of landslide susceptibility in an area, and vice versa.
The distribution of landslide susceptibility levels is consistent with the distributions of slope, aspect, elevation and distance from roads.
The Information Value Model (IVM) method does not include ISSN: 0852-0682.
EISSN: 2460-3945 weighing in its calculation, so all parameters have the same level of equality.
This is one of the weaknesses of the method, because each parameter might have a different level of impact on landslide susceptibility.
Conclusion The results of the spatial modelling using the IVM method were able to describe the spatial distribution of landslide susceptibility levels in Purworejo District.
Although it is known that in general the northern and eastern parts of Purworejo Regency are very prone to landslides, more detailed knowledge is needed for use in landslide disaster mitigation Collaboration between stakeholders in reducing landslide disasters is necessary so that disaster mitigation is effective.
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Evidence-Best Landslide.
(Sudaryatno et al.
Acknowledgements The author would like to thank the Ministry of Research.
Technology and Higher
Education of Republic Indonesia for funding this research through the AuPenelitian Dasar 8QJJXODQ 3HUJXUXDQ 7LQJJLAA 3'837 JUDQW
References