Manifestation of an advanced fuzzy logic model coupled with Geo-information techniques to landslide susceptibility mapping and their comparison with logistic regression modelling

Landslides are very common natural problems in the Selangor area of Malaysia due to the improper use of landcover and tropical rainfall. There are many landslide susceptibility analyses such as statistical, bivariate and data mining approaches exist in the literature. This paper presents the use of fuzzy logic relations for landslide susceptibility mapping on part of Selangor area, Malaysia, using a Geographic Information System (GIS) and remote sensing data. At first, landslide locations were identified in the study area from the interpretation of aerial photographs and satellite images, supported by extensive field surveys. Topographic and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Thirteen landslide conditioning factors such as slope gradient, slope exposure, plan curvature, altitude, stream power index, topographic wetness index, distance from drainage, distance from road, lithology, distance from faults, soil, landcover and normalized difference vegetation index (ndvi) were extracted from the spatial database. These factors were analyzed using fuzzy logic relations to produce the landslide susceptibility maps. Using the landslide conditioning factors and the identified landslides, the fuzzy membership values were calculated. Then fuzzy algebraic operators were applied to the fuzzy membership values for landslide susceptibility mapping. Finally, the ROC curves for all landslide susceptibility models were drawn and the area under curve values were calculated. Landslide locations were used to validate results of the landslide susceptibility maps and the validation results showed 94% accuracy for the fuzzy gamma operator employing all parameters produced in the present study as the landslide conditioning factors. Results showed that, among the fuzzy relations, in the case in which the gamma operator (λ = 0.975) showed the best accuracy (94.73%) while the case in which the fuzzy algebraic Or was applied showed the worst accuracy (84.76%). The landslide susceptibility maps produced by the fuzzy gamma operators shows similar trends as those obtained by applying logistic regression procedure by the same author and indicate that fuzzy relations results perform slightly better than the earlier method. Qualitatively, the model yields reasonable results which can be used for preliminary land-use planning purposes.