Landslide susceptibility mapping: an integrated approach using knowledge-based numerical rating scheme, remote sensing, and multiple overlay analysis

Abstract

Landslides are natural calamities that happen all over the world. Every year, landslides claim the lives of thousands of people and cause significant damage worldwide. The rapid and unpredictable expansion of many cities has a significant impact on the physical environment, with rapidly urbanizing regions of uneven topography where environmental circumstances make the construction of structures and infrastructure impossible or susceptible to instability. Our work proposes the integration of remote sensing, a knowledge-based numerical rating scheme, and multiple overlay analysis methodologies for landslide susceptibility mapping. We evaluate binary overlay, weighted overlay, and fuzzy overlay to map landslide vulnerability in the Ambegaon taluka of Pune district, Maharashtra. The input information used for the prediction and valuation of the landslide susceptibility map (LSM) includes an inventory of 39 active landslide points and 10 potential causality features of landslides: rainfall, slope, aspect, curvature, elevation, Euclidean distance to streamline, normalized difference vegetation index, topographic wetness index, Euclidean distance to roadline, and lithology. The area of consideration was categorized into five main susceptibility groups based on the computed landslide susceptibility scale, ranging from extremely low to severe. The LSM created using the fuzzy gamma operator (k = 0.95) has an overall forecast and prediction accuracy of 89.74%. The optimum value of the gamma operator (k) for this research is 0.95. The LSM was verified by correlating the frequency of landslides on different hazard classes. Implementation of this LSM at the regional level offers a foundation for expanding the approach to other geographies.