Abstract
There are different approaches and techniques for landslide susceptibility mapping. However, no agreement has been reached in both the procedure and the use of specific controlling factors employed in the landslide susceptibility mapping. Each model has its own assumption, and the result may differ from place to place. Different landslide controlling factors and the completeness of landslide inventory may also affect the different result. Incomplete landslide inventory may produce significance error in the interpretation of the relationship between landslide and controlling factor. Comparing landslide susceptibility models using complete inventory is essential in order to identify the most realistic landslide susceptibility approach applied typically in the tropical region Indonesia. Purwosari area, Java, which has total 182 landslides occurred from 1979 to 2011, was selected as study area to evaluate three data-driven landslide susceptibility models, i.e., weight of evidence, logistic regression, and artificial neural network. Landslide in the study area is usually affected by rainfall and anthropogenic activities. The landslide typology consists of shallow translational and rotational slide. The elevation, slope, aspect, plan curvature, profile curvature, stream power index, topographic wetness index, distance to river, land use, and distance to road were selected as landslide controlling factors for the analysis. Considering the accuracy and the precision evaluations, the weight of evidence represents considerably the most realistic prediction capacities (79%) when comparing with the logistic regression (72%) and artificial neural network (71%). The linear model shows more powerful result than the nonlinear models because it fits to the area where complete landslide inventory is available, the landscape is not varied, and the occurence of landslide is evenly distributed to the class of controlling factor.
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References
Atkinson PM, Massari R (1998) Generalised linear modelling of susceptibility to landsliding in the Central Apennines, Italy. Comput Geosci 24(4):373–385
Ayalew L, Yamagishi H (2005) The application of GIS based logistic regression for landslide susceptibility mapping in Kakudo-Yohiko Mountains Central Japan. Geomorphology 65:15–31
Bai S-B, Wang J, Lu G-N, Zou P-G, Hou S-H, Xu S-N (2010) GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology 115:23–31
Bai SB, Wang J, Thiebes B, Cheng C, Chang ZY (2014) Susceptibility assessments of the Wenchuan earthquake-triggered landslides in Longnan using logistic regression. Environ Earth Sci 71:731–743
Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology. Hydrol Sci Bull 24:43–69
Bi R, Schleier M, Rohn J, Ehret D, Xiang W (2014) Landslide susceptibility analysis based on ArcGIS and artificial neural network for a large catchment in Three Gorges region, China. Environ Earth Sci 72:1925–1938
BIG (Indonesian Geospatial Agency) (2001) Peta Rupabumi Digital Indonesia lembar Sendangagung-Wates 1408–232 and 1408–214. Bakosurtanal, Bogor, Indonesia
Bonham-Carter GF (2002) Geographic information systems for geoscientist: modeling with GIS. In: Merriam DF (ed) Computer Methods in the Geosciences, vol 13. Elsevier, New York, pp 302–334
Bonham-Carter GF, Agterberg FP, Wright DF (1989) Weights of evidence modelling: a new approach to mapping mineral potential. Stat Appl Earth Sci 89(9):171–183
Can T, Nefeslioglu HA, Gokceoglu C, Sonmez H, Duman TY (2005) Susceptibility assessments of shallow earthflows triggered by heavy rainfall at three subcatchments by logistic regression analyses. Geomorphology 72:250–271
Catani F, Casagli N, Ermini L, Righini G, Menduni G (2005) Landslide hazard and risk mapping at catchment scale in the Arno River basin. Landslides 2:329–342
Chen X, Chen H, You Y, Chen X, Liu J (2016) Weights-of-evidence method based on GIS for assessing susceptibility to debris flows in Kangding County, Sichuan Province, China. Environ Earth Sci 75:70
Choi J, Oh HJ, Lee C, Lee S (2012) Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial networks models using ASTER images and GIS. Eng Geol 124:12–23
Chung C-JF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogramm Eng Remote Sens 65:1389–1399
Couture R (2011) Landslide Terminology—National Technical Guidelines and Best Practices on Landslides. Geol Surv Canada, Open File 6824 p. 12
Cramer JS (2002) The Origin of Logistic Regression. Tinbergen Institute Discussion Paper. http://dare.uva.nl/document/204. Accessed 29 Dec 2011
Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Dhakal S, Paudyal P (2008a) Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence. Geomorphology 102:496–510
Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Masuda T, Nishino K (2008b) GIS based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environ Geol 54:311–324
Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island Hongkong. Geomorphology 42:213–228
Das I, Stein A, Kerle N, Dadhwal V (2012) Landslide susceptibility mapping along road corridors in the Indian Himalayas using bayesian logistic regression models. Geophys J Roy Astron Soc 179:116–125
Domínguez-Cuesta MJ, Jiménez-Sánchez M, Berrezueta E (2007) Landslides in the Central Coalfield (Cantabrian Mountains, NW Spain): geomorphological features, conditioning factors and methodological implications in susceptibility assessment. Geomorphology 89:358–369
Ercanoglu M (2005) Landslide susceptibility assessment of SE Bartin (West Black Sea region, Turkey) by artificial neural networks. Nat Hazard Earth Syst Sci 5:979–992
Ermini L, Catani F, Casagli N (2005) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66(1–4):327–343
ESRI (Environmental Research Systems Institute, Inc). 2009. ArcGIS Version 9.3. Redlands
Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage WZ (2008) Guidelines for landslide susceptibility, hazard, risk zoning for land-use planning. Eng Geol 102:99–111
Garcia-Rodriguez MJ, Malpica JA (2010) Assessment of earthquake-triggered landslide susceptibility in el Salvador based on Artificial Neural Network model. Nat Hazard Earth Syst Sci 10:1307–1315
Gomez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Eng Geol 78:1–27
Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang KT (2012) Landslide inventory maps: new tools for an old problem. Earth Sci Rev 112:42–66
Hengl T, Maathuis BHP, Wang L (2009) Geomorphometry in ILWIS. In: Hengl T, Reuter HI (eds) Geomorphometry: concepts, software, applications. Developments in soil science, 3rd edn. Elsevier, Amsterdam, pp 497–525
Hosmer DW, Lemeshow S (2000) Applied regression analysis. Wiley, New York
Huabin W, Gangjun W, Weiya X, Gonghui W (2005) GIS-based landslide hazard assessment: an overview. Prog Phys Geogr 29(4):548–567
Kendall M, Stuart A (1979) The advanced theory of statistics: inference and relationship. Griffin, London
Kirschbaum DB, Adler R, Hong Y, Hill S, Lerner-Lam A (2010) A global landslide catalog for hazard application: method, result, and limitations. Nat Hazard 52(3):561–575
Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4(1):33–41
Lee S, Ryu J, Won J, Park H (2004) Determination and application of weights for landslide susceptibility mapping using an artificial neural network. Eng Geol 71:289–302
Lusted LB (1968) Introduction to medical decision making. Charles C. Thomas, Springfield III
McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133
Menard SW (1995) Applied logistic regression analysis. SAGE Publication Inc, Thousand Oaks
Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modeling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5(1):3–30
Nagarajan R, Roy A, Vinod Kumar R, Mukherjee A, Khire MV (2000) Landslide hazard susceptibility mapping based on terrain and climatic factors for tropical monsoon Regions. Bull Eng Geol Env 58(4):275–287
Nefeslioglu HA, Gokceoglu 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 97:171–191
Neuhäuser B, Terhorst B (2007) Landslide susceptibility assessment using “Weights-of-Evidence” applied to a study area at the Jurassic Escarpment (SW-Germany). Geomorphology 86:12–24
O’Brien RM (2007) A caution regarding rules of thumb for variance inflation factors. Qual Quant 41:673–690
Ohlmacher GC, Davis JC (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng Geol 69(33):331–343
Pennock DJ, Zebarth BJ, de Jong E (1987) Landform classification and soil distribution in hummocky terrain, Saskatchewan, Canada. Geoderma 40(297):315
Pradhan B, Lee S (2009) Landslide risk analysis using artificial neural network model focussing on different training sites. Int J Phys Sci 4:001–015
Pradhan B, Lee S (2010) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modeling. Environ Model Softw 25:747–759
Quinn P, Beven K, Chevallier P, Planchon O (1991) The prediction of hillslope paths for distributed hydrological modeling using digital terrain models. Hydrol Process 5:59–79
Rahardjo W, Sukandarrumidi, Rosidi HMD (1995) Peta Geologi Lembar Yogyakarta, Jawa. Pusat Penelitian dan Pengembangan Geologi, Bandung
Remi NR, Giardino JR, Vitek JD (2010) Modelling susceptibility to landslides using weight of evidence approach: Western Colorado, USA. Geomorphology 115:172–187
Samodra G, Chen G, Sartohadi J, Kasama K (2015) Generating landslide inventory by participatory mapping: an example in Purwosari, Yogyakarta, Java. Geomorphology. doi:10.1016/j.geomorph.2015.07.035->
Schicker R, Moon V (2012) Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at regional scale. Geomorphology 161–162:40–57
Süzen ML, Doyuran V (2004) Data driven bivariate landslide susceptibility assessment using GIS: a method and application to Asarsuyu Catchment, Turkey. Eng Geol 71:303–321
Van Den Eeckhaut M, Vanwalleghem T, Poesen J, Govers G, Verstraeten G, Vandekerckhove L (2006) Prediction of landslide susceptibility using rare events logistic regression: a case-study in the Flemish Ardennes, Belgium. Geomorphology 76:392–410
van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphology information in indirect landslide susceptibility assessment. Nat Hazard 30:399–419
Yesilnacar E, Topal T (2005) Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol 79:251–266
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
Zadeh LA (1994) Fuzzy logic, neural networks and soft computing. Fuzzy Systems 37(3):78–84
Acknowledgements
The authors are very grateful to three anonymous reviewers for their detailed and constructive review. We also thank our colleagues in the Department of Environmental Geography, Faculty of Geography, Universitas Gadjah Mada, for their support in conducting the field survey.
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Samodra, G., Chen, G., Sartohadi, J. et al. Comparing data-driven landslide susceptibility models based on participatory landslide inventory mapping in Purwosari area, Yogyakarta, Java. Environ Earth Sci 76, 184 (2017). https://doi.org/10.1007/s12665-017-6475-2
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DOI: https://doi.org/10.1007/s12665-017-6475-2