West Java Province is the most landslide risky area in Indonesia owing to extreme geo-morphological conditions, climatic conditions and densely populated settlements with immense completed and ongoing development activities. So, a landslide susceptibility map at regional scale in this province is a fundamental tool for risk management and land-use planning. Logistic regression and Artificial Neural Network (ANN) models are the most frequently used tools for landslide susceptibility assessment, mainly because they are capable of handling the nature of landslide data. The main objective of this study is to apply logistic regression and ANN models and compare their performance for landslide susceptibility mapping in volcanic mountains of West Java Province. In addition, the model application is proposed to identify the most contributing factors to landslide events in the study area. The spatial database built in GIS platform consists of landslide inventory, four topographical parameters (slope, aspect, relief, distance to river), three geological parameters (distance to volcano crater, distance to thrust and fault, geological formation), and two anthropogenic parameters (distance to road, land use). The logistic regression model in this study revealed that slope, geological formations, distance to road and distance to volcano are the most influential factors of landslide events while, the ANN model revealed that distance to volcano crater, geological formation, distance to road, and land-use are the most important causal factors of landslides in the study area. Moreover, an evaluation of the model showed that the ANN model has a higher accuracy than the logistic regression model.

1.
Sterlacchini
,
S.
,
Ballabio
,
C.
,
Blahut
,
J.
,
Masetti
,
M.
,
Sorichetta
,
A.
:
Spatial agreement of predicted patterns in landslide susceptibility maps
,
Geomorphology
,
125
,
51
61
,
2011
.
2.
Nefeslioglu
,
H.A.
,
Gokceoglu
,
C.
,
Sonmez
,
H.
:
An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps
,
Engineering Geology
,
97
,
171
191
,
2008
.
3.
Wati
,
S.E.
,
Hastuti
,
T.
,
Widjojo
,
S.
,
Pinem
,
F.
,
2010
.
Landslide susceptibility mapping with heuristic approach in mountainous area: a case study in Tawangmangu sub district, Central Java, Indonesia
.
International Archives of the Photogrammetry, Remote Sensing, and Spatial Information Science
, Vol.
XXXVIII
Part 8, Kyoto, Japan
.
4.
Hadmoko
,
D.S.
,
Lavigne
,
F.
,
Sartohadi
,
J.
,
Hadi
,
P.
,
Winaryo: Landslide hazard and risk assessment and their application in risk management and landuse planning in eastern flank of Menoreh Mountains, Yogyakarta Province, Indonesia
,
Natural Hazard
,
54
,
623
642
,
2010
.
5.
Bachri
,
S.
,
Shresta
,
R.P.
:
Landslide hazard assessment using analytical hierarchy processing (AHP) and geographic information system in Kaligesing mountain area of Central Java Province Indonesia
,
5ᵗʰ Annual International Workshop and Expo on Sumatra Tsunami Disaster and Recovery
,
Indonesia
,
2010
.
6.
Oh
,
H.-J.
,
Lee
,
S.
,
Soedradjat
,
G.M.
:
Quantitative landslide susceptibility mapping in Pemalang area, Indonesia
,
Environmental Earth Science
,
60
,
1317
1328
,
2010
.
7.
Ngadisih
,
Ryuichi
Yatabe
,
Netra P.
Bhandary
,
Ranjan K.
Dahal
:
Integration of statistical and heuristic approaches for landslide risk analysis: a case of volcanic mountains in West Java Province, Indoneis
,
Georisk
,
8
(
1
),
29
47
.
2014
8.
Corominas
,
J.
,
Moya
,
J.
:
A review of assessing landslide frequency for hazard zoning purposes
,
Engineering Geology
,
102
,
193
213
,
2008
.
9.
Yalcin
,
A.
:
GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparison of results and confirmations
,
Catena
,
72
,
1
12
,
2008
.
10.
Yesilnacar
,
E.
,
Topal
,
T.
:
Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey)
,
Engineering Geology
,
79
,
251
266
,
2005
.
11.
Lee
,
S.
,
Ryu
,
J.-H.
,
Min
,
K.
,
Won
,
J.-S.
:
Landslide susceptibility analysis using GIS and artificial neural network
,
Earth Surface Process and Landforms
,
28
,
1361
1376
,
2003
.
12.
Lee
,
S.
,
Ryu
,
J.-H.
,
Won
,
J.-S.
,
Park
,
H.-J.
:
Determination and application of the weights for landslide susceptibility mapping using an artificial neural network
,
Engineering Geology
,
71
,
289
302
,
2004
.
13.
Lee
,
S.
,
Ryu
,
J.-H.
,
Kim
,
I.-S.
,
Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea
,
Landslide
,
4
,
327
338
,
2007
.
14.
Ercanoglu
,
M.
:
Landslide susceptibility assessment of SE Bartin (West Black Sea region, Turkey) by artificial neural networks
,
Natural Hazard and Earth Sciences
,
5
,
979
992
,
2005
.
15.
Ermini
,
L.
,
Catani
,
F.
,
Casagli
,
N.
:
Artificial neural networks applied to landslide susceptibility
,
Geomorphology
,
66
,
327
343
,
2005
.
16.
Choi
,
J.
,
Oh
,
H.-J.
,
Lee
,
C.
,
Lee
,
S.
:
Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial networks models using ASTER images and GIS
,
Engineering Geology
,
124
,
12
23
,
2012
.
17.
Hosmer
,
D.W.
,
Lemeshow
,
S.
:
Applied logistic regression
: second edition.
John Wiley & Sons, Inc
,
United States of America
,
2002
.
18.
Cox
,
D.R.
, and
Snell
,
E.J.
:
Analysis of binary data
,
1989
19.
Nagelkerke
,
N.J.D.
: A note on a general definition of the coefficient of determination, 1991, in
Pampel
,
F.C.
:
Logistic regression: a primer, Sage University papers series on quantitative applications in the social sciences
, series no. 07-132,
2000
.
20.
Clark
,
W.A.
,
Hosking
,
P.L.
:
Statistical methods for geographers, 1986
, in:
Ayalew
,
L.
,
Yamagishi
,
H.
:
The application of GIS based logistic regression for landslide susceptibility mapping in Kakudo-Yohiko Mountains, Central Japan
,
Geomorphology
,
65
,
15
31
,
2005
.
21.
Can
,
T.
,
Hakan
,
A.
,
Nefeslioglu
,
Gokceoglu
,
C.
,
Sonmez
,
H.
,
Dumam
,
T.Y.
:
Susceptibility assessment of shallow earthflows triggered heavy rainfall at three catchments by logistic regression analyses
,
Geomorphology
72
,
250
271
,
2005
.
22.
Das
,
I.
,
Sahoo
,
S.
,
Van Westen
,
C.
,
Stein
,
A.
,
Hack
,
R.
:
Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the Northern Himalaya (India)
,
Geomorphology
,
114
,
627
637
,
2010
.
23.
Nandi
,
A.
, and
Shakoor
,
A.
:
A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses
,
Engineering Geology
,
110
,
11
20
,
2009
.
24.
Pradhan
,
B.
,
Lee
,
S.
:
Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling
,
Environmental Modelling and Software
,
25
,
747
759
,
2010
.
25.
Bai
,
S-B.
,
Wong
,
J.
,
Lu
,
G-N.
,
Zhou
,
P.
,
Hou
,
S-S.
,
Xu
,
S-N.
:
GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the three Gorges area, China
,
Geomorphology
115
,
23
31
,
2010
.
26.
Van de Eeckhaut
,
M.
,
Vanwalleghem
,
T.
,
Poesen
,
J.
,
Govers
,
G.
,
Verstraeten
,
G.
,
Vandekerckhove
,
L.
:
Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium)
,
Geomorphology
,
76
,
392
410
,
2006
.
27.
Zhu
,
L.
,
Jing-Feng
,
H.
:
GIS-based logistic regression method for landslide susceptibility mapping in regional scale
,
Zhejiang University Science A
,
7
(
12
),
2007
2017
,
2006
.
28.
Lin
,
H-M.
,
Chang
,
S.-K
,
Wu
,
J.-H
,
Juang
,
H.
,
Neural network based model for assessing failure potential of highway slopes in the Alishan, Taiwan Area: Pre- and post-earthquake investigation
,
Engineering Geology
,
104
,
280
289
,
2009
.
29.
Pradhan
,
B.
,
Lee
,
S.
,
Buchroithner
,
M.F.
:
A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses
,
Computer, Environmental and Urban Systems
,
34
,
216
235
,
2010
.
30.
Yilmaz
,
I.
:
Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslide (Tokat Turkey)
,
Computer and Geosciences
,
35
,
1125
1138
,
2000
.
31.
Pradhan
,
B.
,
Lee
,
S.
:
Landslide risk analysis using artificial neural network model focusing on different training sites
,
Physical Sciences
,
4
(
1
),
001
015
,
2009
.
32.
Ramani
,
S.E.
,
Pitchaimani
,
K.
,
Gnanamanickam
,
V.R.
:
GIS based-landslide susceptibility mapping of Tevankarai Ar Sub-watershed, Kodaikhanal, India using binary logistic regression analysis
,
Mountain Science
,
8
,
505
517
,
2011
.
33.
Ruff
,
M.
,
Czurda
,
K.
:
Landslide susceptibility analysis with a heuristic approach in the Eastern Alpn (Vorarlerg, Austria)
,
Geomorphology
,
94
,
314
324
, 2008.
34.
Van Westen
,
C.J.
,
Terlien
,
T.J.
:
An approach towards deterministic landslide hazard analysis in GIS: A case study from Manizales (Colombia)
,
Earth Surf Proc Landforms
,
21
,
853
868
,
1996
.
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