Evolution and Critical Evaluation of Deterministic Physically Based Rainfall-Induced Landslide Susceptibility Mapping: A Mixed Review

Abstract

Physically-based models play a critical role in assessing rainfall-induced landslide susceptibility, offering valuable insights into landslide hazard prediction and risk mitigation. This study conducts a scientometric and systematic review of deterministic, physically-based, rainfall-induced landslide susceptibility mapping, synthesising research trends and advancements in this domain. A comprehensive literature search, conducted through the Scopus database following PRISMA guidelines, identifying 70 key studies for in-depth analysis. The findings reveal significant progress, including the integration of climate change projections, enhanced real-time monitoring systems, and advancements in high-resolution data processing. Despite these developments, challenges persist in achieving a balance between model complexity and practical applicability. This review highlights the need for standardised validation protocols, robust uncertainty analysis, and interdisciplinary approaches that merge physical modelling with machine learning techniques. By evaluating the evolution and current state of deterministic physically-based landslide modelling, this study provides a valuable reference for researchers and practitioners, contributing to the advancement of more reliable and accessible landslide susceptibility assessments.