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<p>For the safe and resilient development of cities, disaster risk reduction plays a crucial role, aligning with sustainable development goal 11 of the United Nations. Supporting this objective, the present study developed a machine learning (ML) classifier-based threshold model to determine rainfall thresholds for predicting impending landslides in Kerala, India, using historical data. Using a dataset of 64 rainfall-induced landslide events recorded since the year 2000, rainfall data were collected up to 15&#xa0;days prior to each landslide to support empirical analysis of intensity-duration and event rainfall-duration thresholds. In cases where exact rainfall durations were unavailable, classification machine learning (ML) models, including K-nearest neighbours (KNN), random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), and logistic regression, were used to determine threshold reliability. Among these, the KNN model with 5 Neighbours achieved the highest performance, with an ROC-AUC of 0.9 and an accuracy of 82%. This model, saved as a pickle file, serves as a core filter in the development of a landslide early warning system. This paper presents the model development and performance comparisons, contributing to a practical, community-centred solution for landslide disaster resilience in Kerala.</p>

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Empirical and machine learning-based approaches to identify rainfall thresholds for landslide prediction: a case study of Kerala, India

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月6日

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  • Varun Menon,
  • Sreevalsa Kolathayar

摘要

For the safe and resilient development of cities, disaster risk reduction plays a crucial role, aligning with sustainable development goal 11 of the United Nations. Supporting this objective, the present study developed a machine learning (ML) classifier-based threshold model to determine rainfall thresholds for predicting impending landslides in Kerala, India, using historical data. Using a dataset of 64 rainfall-induced landslide events recorded since the year 2000, rainfall data were collected up to 15 days prior to each landslide to support empirical analysis of intensity-duration and event rainfall-duration thresholds. In cases where exact rainfall durations were unavailable, classification machine learning (ML) models, including K-nearest neighbours (KNN), random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), and logistic regression, were used to determine threshold reliability. Among these, the KNN model with 5 Neighbours achieved the highest performance, with an ROC-AUC of 0.9 and an accuracy of 82%. This model, saved as a pickle file, serves as a core filter in the development of a landslide early warning system. This paper presents the model development and performance comparisons, contributing to a practical, community-centred solution for landslide disaster resilience in Kerala.