Approaches for comparative evaluation of raster GIS-based landslide susceptibility zonation maps

Abstract Evaluation of maps generated from different conceptual models or data processing approaches at spatial level has importance in many geoenvironmental applications. This paper addresses the spatial comparison of different landslide susceptibility zonation (LSZ) raster maps of the same area derived from various procedures. In hilly regions such as the Himalayas, occurrence of landslides is frequent, which necessitates the study of landslides in the region for future developmental planning. A critical aspect in landslide studies is the procedure for assignment of weights to various causative factors affecting the occurrence of landslides. A detailed study on conventional, artificial neural network (ANN) black box, fuzzy set based and combined neural and fuzzy weighting procedures for LSZ mapping in the Himalayas has recently been published by the authors in [Kanungo, D.P., Arora, M.K., Sarkar, S., Gupta, R.P., 2006. A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Engineering Geology 85, 347–366]. The evaluation of various maps in that study was however based only on comparison of areal extents of various landslide susceptibility zones. In this paper, we present a spatial level comparative evaluation of those maps to get a detailed insight into the performance of each of the weighting procedures for landslide susceptibility zonation. The evaluation has been done through three approaches, viz., landslide density analysis, error matrix analysis and difference image analysis. Based on the landslide density values, it is inferred that the combined neural and fuzzy procedure for LSZ mapping appears to be significantly better than other procedures. The error matrix analysis highlights the significant difference between the conventional subjective weight assignment procedure and the objective combined neural and fuzzy procedure. Finally, the significant influence of a causative factor has been revealed by difference image analysis. The use of these spatial evaluation approaches in tandem may be highly beneficial to quantitatively assess the landslide susceptibility zonation or any other such maps.

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