Landslide susceptibility mapping for disaster risk reduction using statistical and MCDM approaches in Wayanad District, Kerala, India

Landslide susceptibility mapping is important for the disaster risk reduction and sustainable land-use planning, especially in areas with growing climate variability and anthropogenic disturbances. The study was carried out in Wayanad district of Kerala state, India with a view to identify and analyse the landslide prone areas. The landslide susceptibility was measured using a set of 17 conditioning factors such as topographical, hydrological, geological, geomorphological and anthropogenic parameters. The landslide susceptibility maps were generated using both statistical and multi-criteria decision making (MCDM) analysis methods such as frequency ratio (FR), logistic regression (LR), TOPSIS and VIKOR, to produce and compare susceptibility maps. The base was the landslide inventory dataset of 2018 that was split into training (70%), and testing (30%), to validate the model. Model performance was validated using receiver operating characteristic (ROC) curve and area under curve (AUC) analyses, with VIKOR (88%) being the most accurate the next in order being TOPSIS (84%), logistic regression (82%) and frequency ratio (79%). And at last the ability of the statistical models to deal with this problem was further validated by the cross-validation and yielded mean ROC–AUC values of 0.860 and 0.864 for the LR and FR models, respectively, indicating good predictive performance on various subsets. Areas that were found to be highly susceptible included areas like Sultan Bathery, Vythiri and Mananthavady, which were mainly steep with heavy rainfall patterns. The results reflect the success of such a combination of approaches in identifying vulnerable areas and assisting disaster mitigation in the Western Ghats area. In addition to mapping landslide prone areas, the proposed integrated modelling framework will establish a scientific foundation for sustainable land-use planning, disaster risk reduction and infrastructure development for climate change adaptation in the ecologically critical region of Western Ghats. The results provide evidence-based decision-making on how to lower environmental and socio-economic vulnerabilities and directly drive the implementation of sustainable development goals (SDGs) that are directly relevant to the SDGs 11: Sustainable Cities and Communities, SDG 13: Climate Action and SDG 15: Life on Land.