Application of landslide susceptibility towards urbanization suitability zonation in mountainous settings

Abstract

Amplified susceptibility to landslides poses challenges to sustainable and equitable urban development. Thus, in this study, we introduce an artificial intelligence and geographic information system-based approach, urbanization suitability zonation (USZ), for spatial assessment of land suitability for urbanization, which accounts multiple factors, including a focus on landslide susceptibility. Further, the proposed USZ is tailored to assess health of current urbanization patterns, depicting the driving factors behind it by establishing a best-case urbanization suitability (BUS) and weighted urbanization suitability (WUS). The BUS portrays a hypothetically ideal land suitability scenario as a function of equally weighted factors, while the WUS portrays land suitability changes when factors are weighted hierarchically. An examination of current development against the BUS provides idealness of current urbanization patterns. The WUS aids in assessing the significance of factors in influencing the current urbanization pattern, and checks its resemblance with the BUS. The mountainous Indian township of Mussoorie is selected as the study area to exercise the USZ by considering seven factors with different weights, and subsequently generated a BUS map and 7 WUS maps (WUS–I to VII), which all display urbanization suitability distribution in five different classes. The BUS map shows that ∼98 % of current development falls in higher-suitability areas, indicating its preferability. Among the different WUS maps, the WUS-II aligns best with the BUS and current built-up, with slope gradient and transportation connectivity as key factors, and according to it, high-suitability areas are generally the central and south-facing slopes, while north-facing slopes are unsuitable, in general.