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<p>Landslides are among the most damaging geo-environmental hazards in the humid tropical mountain belts of the Western Ghats, India. In Kerala, the frequency and intensity of rainfall-induced landslides have increased markedly in recent decades, particularly during the extreme monsoon events of 2018. This study evaluates landslide susceptibility and rainfall thresholds in the two highland local self-governments (LSGs), Konnathady and Vellathooval Grama Panchayats of Idukki District in Kerala, a region highly vulnerable to slope failures. A GIS-based Multiple Linear Regression (MLR) model was developed using fourteen terrain, hydrological, vegetation, environmental, and anthropogenic conditioning factors. A landslide inventory comprising 146 events was compiled, with 70% of the inventory used for model development and the remaining 30% reserved for independent validation. Multicollinearity assessment using the Variance Inflation Factor (VIF) indicated negligible interdependence among the predictor variables, supporting the statistical robustness of the model. The resulting landslide susceptibility map shows that 30.01% of the study area falls within high and very high susceptibility classes, with Konnathady exhibiting a greater concentration of susceptible terrain than Vellathooval. Validation of the model yielded an Area Under the Curve (AUC) value of 0.796, indicating good predictive capability. Additional validation metrics produced an overall accuracy of 79.1%, precision of 77.8%, recall of 81.4%, specificity of 76.7%, an F1-score of 79.6%, and a Cohen’s Kappa coefficient of 0.581, confirming satisfactory agreement between observed and predicted landslide occurrences. Among the conditioning factors, slope and proximity to roads emerged as the most influential controls on landslide occurrence. Analysis of the 2018 extreme rainfall event revealed that five-day cumulative antecedent rainfall was the principal triggering mechanism, with critical thresholds of approximately 300&#xa0;mm (13 August 2018) and 500&#xa0;mm (15 August 2018) identified prior to widespread slope failures. The findings highlight the combined influence of terrain characteristics, rainfall variability, and human interventions on landslide susceptibility and emphasize the importance of integrating susceptibility mapping, rainfall thresholds, and risk-informed planning into land-use management, infrastructure development, and landslide early-warning systems across the Western Ghats.</p>

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Landslide susceptibility and local antecedent rainfall thresholds in the Highland Western Ghats Kerala India

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月17日

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  • A. Aiswarya Nair,
  • A. S. Amrutha,
  • G. S. Pradeep,
  • H. Vijith

摘要

Landslides are among the most damaging geo-environmental hazards in the humid tropical mountain belts of the Western Ghats, India. In Kerala, the frequency and intensity of rainfall-induced landslides have increased markedly in recent decades, particularly during the extreme monsoon events of 2018. This study evaluates landslide susceptibility and rainfall thresholds in the two highland local self-governments (LSGs), Konnathady and Vellathooval Grama Panchayats of Idukki District in Kerala, a region highly vulnerable to slope failures. A GIS-based Multiple Linear Regression (MLR) model was developed using fourteen terrain, hydrological, vegetation, environmental, and anthropogenic conditioning factors. A landslide inventory comprising 146 events was compiled, with 70% of the inventory used for model development and the remaining 30% reserved for independent validation. Multicollinearity assessment using the Variance Inflation Factor (VIF) indicated negligible interdependence among the predictor variables, supporting the statistical robustness of the model. The resulting landslide susceptibility map shows that 30.01% of the study area falls within high and very high susceptibility classes, with Konnathady exhibiting a greater concentration of susceptible terrain than Vellathooval. Validation of the model yielded an Area Under the Curve (AUC) value of 0.796, indicating good predictive capability. Additional validation metrics produced an overall accuracy of 79.1%, precision of 77.8%, recall of 81.4%, specificity of 76.7%, an F1-score of 79.6%, and a Cohen’s Kappa coefficient of 0.581, confirming satisfactory agreement between observed and predicted landslide occurrences. Among the conditioning factors, slope and proximity to roads emerged as the most influential controls on landslide occurrence. Analysis of the 2018 extreme rainfall event revealed that five-day cumulative antecedent rainfall was the principal triggering mechanism, with critical thresholds of approximately 300 mm (13 August 2018) and 500 mm (15 August 2018) identified prior to widespread slope failures. The findings highlight the combined influence of terrain characteristics, rainfall variability, and human interventions on landslide susceptibility and emphasize the importance of integrating susceptibility mapping, rainfall thresholds, and risk-informed planning into land-use management, infrastructure development, and landslide early-warning systems across the Western Ghats.