Selection of weightages for causative factors used in preparation of landslide susceptibility zonation (LSZ)

Abstract

Most of the models for landslide susceptibility zonation (LSZ) except machine learning needs manual selection of weights or ratings, which are given by expertise knowledge, leading to subjectivity and specificity. Hence, selection of ratings is very important in the preparation of LSZ maps. Here, seven layers/factors viz. aspect, elevation, geology, slope, soil, distance from stream, distance from thrusts are considered for LSZ mapping in Mandakini River basin, Uttarakhand containing 1,805,636 pixels. The weights were calculated using information value (InfoVal) method. The occurrences of landslides (2009 pixels) until 2008 were considered for training of model. Thus for giving rating to each thematic layer, 7! = 5040 permutations (ratings) are possible. Hence, 5040 LSZ maps were prepared and out of them, the best rating was identified using fisher discriminant analysis (FDA) and binary logistic regression (LR). FDA and LR gave similar ratings and the correlation (r2) between their weights was 0.9226. Thematic layers were then multiplied by the corresponding ratings and added to prepare the final LSZ map. The results were validated on the landslide data until 2011, which were not used for training. Out of 223 occurrences of landslides, 39% (87) falls in high susceptible zone followed by 35% (79) in very high susceptible zone according to FDA and similar for LR. The accuracy of the prediction was assessed by Heidke-Skill-Score, which gave score of 0.89 to FDA and 0.90 to LR for 0.5 as threshold of landslide susceptibility index.

Keywords

• Fisher discriminant analysis (FDA)
• Heidke-Skill-Score (HSS)
• information value (InfoVal)
• landslide susceptibility zonation (LSZ)
• logistic regression (LR)