Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS

Authors:
Dieu Tien Bui;Biswajeet Pradhan;Owe Lofman;Inge Revhaug;Oystein B. Dick
Affiliations:
Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, P.O. Box 5003-IMT, N-1432 Aas, Norway and Faculty of Surveying and Mapping, Hanoi University of Mining an ...;Institute of Advanced Technology, Spatial and Numerical Modelling Laboratory, University Putra Malaysia, Serdang, Selangor Darul Ehsan 43400, Malaysia;Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, P.O. Box 5003-IMT, N-1432 Aas, Norway;Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, P.O. Box 5003-IMT, N-1432 Aas, Norway;Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, P.O. Box 5003-IMT, N-1432 Aas, Norway
Venue:
Computers & Geosciences
Year:
2012

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Abstract

The objective of this study is to investigate a potential application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Geographic Information System (GIS) as a relatively new approach for landslide susceptibility mapping in the Hoa Binh province of Vietnam. Firstly, a landslide inventory map with a total of 118 landslide locations was constructed from various sources. Then the landslide inventory was randomly split into a testing dataset 70% (82 landslide locations) for training the models and the remaining 30% (36 landslides locations) was used for validation purpose. Ten landslide conditioning factors such as slope, aspect, curvature, lithology, land use, soil type, rainfall, distance to roads, distance to rivers, and distance to faults were considered in the analysis. The hybrid learning algorithm and six different membership functions (Gaussmf, Gauss2mf, Gbellmf, Sigmf, Dsigmf, Psigmf) were applied to generate the landslide susceptibility maps. The validation dataset, which was not considered in the ANFIS modeling process, was used to validate the landslide susceptibility maps using the prediction rate method. The validation results showed that the area under the curve (AUC) for six ANFIS models vary from 0.739 to 0.848. It indicates that the prediction capability depends on the membership functions used in the ANFIS. The models with Sigmf (0.848) and Gaussmf (0.825) have shown the highest prediction capability. The results of this study show that landslide susceptibility mapping in the Hoa Binh province of Vietnam using the ANFIS approach is viable. As far as the performance of the ANFIS approach is concerned, the results appeared to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.