Keywords
weight of evidence
information value
Cianjur
How to Cite
Abstract
DOI:10.17014/ijog.7.1.51-63
The main purpose of this paper is to compare the performance of bivariate statistical models i.e. Frequency Ratio, Weight of Evidence, and Information Value for landslide susceptibility assessment. These models were applied in Cianjur Regency, West Java Province (Indonesia), in order to map the landslide susceptibility and to rate the importance of landslide causal factors. In the first stage, a landslide inventory map and the input layers of the landslide conditioning factors were prepared in the Geographic Information System (GIS) supported by field investigations and remote sensing data. The 298 landslides were randomly divided into two groups of modeling/training data (70%) and validation/test data sets (30%). The landslide conditioning factors considered for the studied area were slope angle, elevation, slope aspect, lithological unit, and land use. Subsequently, the thematic data layers of conditioning factors were integrated by frequency ratio (FR), weight of evidence (WofeE), and information value (IV). Model performance was tested with receiver operator characteristic analysis. The validation findings revealed that the three models showed promising results since the models gave good accuracy values. The success rates of FR, WofE, and IV models were 0.920, 0.926, and 0.930, while the prediction rates of the three models were 0.913, 0.912, and 0.895, respectively. However, the FR model was proved to be relatively superior in estimating landslide susceptibility throughout the studied area.
References
Agterberg FP, Bonham-Carter GF, Cheng Q, Wright DF (1993) Weights of evidence modeling and weighted logistic regression for mineral potential mapping. In: Davis JC, Herzfeld UC (eds) Computers in geology, 25 years of progress. Oxford University Press, Oxford:13–32
Akıncı H, Dogan S, Kılıçoğlu C (2017) Landslide susceptibility mapping of canik (samsun) district using bayesian probability and frequency ratio models. the Selcuk Int Sci Conf on Appl Sci (ISCAS) 2016:283-299. Doi:10.15317/Scitech.2017.89
Arifianti Y, Agustin F (2017) An assessment of the effective geofactors of landslide susceptibility: case study Cibeber, Cianjur, Indonesia. In: Yamagishi, Bhandary (eds) GIS Landslide. Springer, Tokyo, pp 183-195
Bai SB, Wang J, Zhou PG (2010) GIS-based and logistic regression for landslide susceptibility mapping of Zhongxian segment in the Three Gorge area, China. Geomorphology 115(1–2):23–31. doi: 10.1016/j.geomorph.2009.09.025
Barbieri G, Cambuli P (2009) The weight of evidence statistical method in landslide susceptibility mapping of the Rio Pardu Valley (Sardinia, Italy). 18th World IMACS / MODSIM Congress, Cairns, Australia p.2658-2664.
Bonham-Carter GF (1994) Geographic information systems for geoscientists: modeling with GIS. In: Bonham-Carter F (ed) Computer methods in the geosciences. Pergamon, Oxford, p 398
Chen W, Li W (2014) Application of weights-of-evidence model in landslide susceptibility mapping at Baozhong Region in Baoji, China. EJGE 19 p.791-810.
Chen Z, Liang S, Ke Y, Yang Z, Zhao H (2017) Landslide susceptibility assessment using evidential belief function, certainty factor and frequency ratio model at Baxie River basin, NW China, Geocarto International. doi:10.1080/10106049.2017.1404143
Choi J, Oh HJ, Lee HJ, Lee C, Lee S (2012) Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng Geol 124:12–23
Chung CJF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogrammetry. Eng Remote Sens 65(12):1389–1399. Doi:10.1002/9780470012659.ch4
Chung CJF, Fabbri AG (2003) Validation of Spatial Prediction Models for Landslide Hazard Mapping. S. Nat Hazards 30(3):451-472. doi: 10.1023/B:NHAZ.0000007172.62651.2b
Chung, C.J.F., Fabbri, A.G., and van Westen, C.J., 1995. Multivariate regression analysis forlandslide hazard zonation. In: Carrara, A. and Guzzetti, F. (eds.). Geographical Information Systems in Assessing Natural Hazards. Kluwer Academic Publisher, Dordrecht, The Netherlands, p.107-133.
Conforti M, Aucelli PP, Robustelli G, Scarciglia F (2011) Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (northern Calabria, Italy). Nat. Hazards 56(3):881–898
Constantin M, Bednarik M, Jurchescu MC, Vlaicu M (2011) Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environ Earth Sci 63:397–40
Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Masuda T, Nishino K (2008) GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Springer Verlag Environ Geol 54:311–324 DOI:10.1007/s00254-007-0818-3
Dou J, Tien Bui D, P. Yunus A, Jia K, Song X, Revhaug I, (2015) Optimization of causative factors for landslide susceptibility evaluation using remote sensing and GIS data in parts of Niigata, Japan. PLoS ONE 10(7): e0133262
Elmoulat M, Brahim LA, Mastere M, Jemmah AI (2015) Mapping of mass movements susceptibility in the Zoumi Region using satellite image and GIS technology (Moroccan Rif). Int. Journal of Sci. & Eng. Res. 6(2):210-217
Ercanoğlu M, Temiz FA (2011) Application of logistic regression and fuzzy operators to landslide susceptibility assessment in Azdavay (Kastamonu, Turkey). Environ Earth Sci 64: 949–964 DOI:10.1007/s12665-011-0912-4
Ghosh, S., 2011. Knowledge guided empirical prediction of landslide hazard. Enschede: University of Twente, Faculty of GeoInformation Science and Earth Observation (ITC), the
Netherland.
Gokceoglu C, Sezer E (2009) A statistical assessment on international landslide literature (1945–2008). Landslides 6:345–351. doi: 10.1007/s10346-009-0166-3
Hussin HY, Zumpano V, Reichenbach P, Sterlacchini S, Micu M, van Westen C, Bălteanu D (2015) Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model. Geomorphology 253:508–523. Doi:10.1016/j.geomorph.2015.10.030
Kayastha P, Dhital M, Smedt FD (2012) Landslide susceptibility mapping using the weight of evidence method in the Tinau Watershed, Nepal. F. Nat Hazards 63(2):479-498 DOI:10.1007/s11069-012-0163-z
Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40(9):1095–1113. Doi:10.1007/s002540100310
Lee S, Pradhan B (2006) Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. J Earth Syst Sci 115 (6):661–667 DOI:10.1007/s12040-006-0004-0
Lin ML, Tung CC (2004) A GIS-based potential analysis of the landslides induced by the Chi-Chi earthquake. Eng Ge 71(1-2):63-77. doi: 10.1016/S0013-7952(03)00126-1
Mezughi T, Akhir JM, Rafek AG, Abdullah I (2011) A multi-class weight of evidence approach for landslide susceptibility mapping applied to an area along the E-W Highway (Gerik – Jeli), Malaysia. EJGE Vol 16:1259–1273. doi: 10.1007/s11053-007-9043-8
Mohammady M, Pourghasemi HR, Pradhan B (2012) Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster-Shafer, and weights-ofevidence models. J Asian Earth Sci 61:221–236. doi:10.1016/j.jseaes.2012.10.005
Mondal S, Maiti R (2012) Landslide susceptibility analysis of Shiv-Khola Watershed, Darjiling: a remote sensing and GIS based analytic hierarchy process. J Indian Soc RS 3:483-496
Nefeslioglu HA, Gökceoglu C, Sonmez H (2008) An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol 97(3)171–191. doi:10.1016/j.enggeo.2008.01.004
Neuhäuser B, Damm B, Terhorst B (2011) GIS-based assessment of landslide susceptibility on the base of the Weights-of Evidence model. Landslides 9(4):511–528. doi: 10.1007/s10346-011-0305-5
Ozdemir A (2011) Landslide susceptibility mapping using bayesian approach in the Sultan Mountains (Aksehir, Turkey). S. Nat Hazards 59(3):1573–1607. doi:10.1007/s11069-011-9853-1
Pardeshi SD, Autade SE, Pardeshi SS (2013) Landslide hazard assessment: recent trends and techniques. SpringerPlus 2(1):523. doi:10.1186/21931801-2-523
Park S, Choi C, Kim B, Kim J (2013) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environ Earth Sci 68:1443–1464
Poli S, Sterlacchini S (2007) Landslide representation strategies in susceptibility studies using weights-of evidence modeling technique. S. Nat Resour Res 16(2):121-134
Pradhan B, Lee S (2010a) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sci 60(5): 1037–1054
Pradhan B, Lee S (2010b) Landslide susceptibility assessment and factor effectanalysis: back-propagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modeling. Environ Model Softw 25(6):747–75 DOI:10.1016/j.envsoft.2009.10.016
Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A (2014) Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. S. Arab J Geosci 7(2):725-742. doi:10.1007/s12517-012-0807-z
Sarkar S, Anbalagan R (2008) Landslide hazard zonation mapping and comparative analysis of hazard zonation maps. J Mountain Sci 5:232–240 DOI:10.1007/s11629-008-0172-2
Sharma M, Kumar R (2008) GIS-based landslide hazard zonation: a case study from the Parwanoo area, Lesser and Outer Himalaya, H.P., India. B Eng Geol Environ 67:129–137 DOI:10.1007/s10064-007-0113-2
Süzen ML, Doyuran V (2004) Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey. Eng Geol 71 (3):303–321. Doi: 10.1016/s00137952(03)00143-1
Teerarungsigul S, Torizin J, Fuchs M, Kühn F, Chonglakmani C (2015) An integrative approach for regional landslide susceptibility assessment using weight of evidence method: a case study of Yom River Basin, Phrae Province, Northern Thailand. Landslides. doi:10.1007/s10346-015-0659-1
Tien Bui D, Pradhan B, Lofman O, Revhaug I (2012) Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models. Math Probl Eng 2012: 1–26. doi: 10.1155/2012/974638
Vakhshoori V, Zare M (2016) Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods, Geomatics, Natural Hazards and Risk, 7:5, 1731-1752, DOI: 10.1080/19475705.2016.1144655
Van Bemmelen, R.W., 1949. The geology of Indonesia, Government Printing Office, The Hague, 732pp.
Vijith H, Madhu G (2007) Application of GIS and frequency ratio model in mapping the potential surface failure sites in the Poonjar sub-watershed of Meenachil river in Western Ghats of Keral. Jo Ind Soc Re Sen 35(3):275-285. doi: 10.1007/BF03013495
Wang J, Yin K, Xiao L (2014) Landslide susceptibility assessment based on GIS and weighted information valuea case study of Wanzhou district, Three gorges reservoir. Chin J Rock Mech Eng 33:797–808
Xu C, Xu X, Yao X, Dai F (2014) Three (nearly) complete inventories of landslides triggered by the May 12, 2008 Wenchuan Mw 7.9 earthquake of China and their spatial distribution statistical analysis. Landslides 11(3):441–461. doi:10.1007/s10346-013-0404-6
Yalcin A, Reis S, Aydinoglu AC, Yomralioglu T (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena, 85(3), 274–287 DOI:10.1016/j.catena.2011.01.014
Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison : A case study from Kat landslides (Tokat-Turkey). Comput Geosci 35:1125–1138 DOI:10.1016/j.cageo.2008.08.007
Yin KL, Yan TZ (1988) Statistical prediction models for slope instability of metamorphosed rocks. in proceedings of the 5th International Symposium on Landslides, Lausanne, Switzerland, 2, p.1269-1272.
Zhu AX, Wang R, Qiao J, Qin CZ, Chen Y, Liu J, Du F, Yang L, Zhu T (2014) An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic. Geomorphology 214:128-138 DOI:10.1016/j.geomorph.2014.02.003