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Generating a Landslide Susceptibility Map Using Integrated Meta-Heuristic Optimization and Machine Learning Models

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  • Tuba Bostan

    (Civil Engineering Department, Faculty of Civil, Yildiz Technical University, 34220 Istanbul, Türkiye)

Abstract

A landslide susceptibility assessment is one of the critical steps in planning for landslide disaster prevention. Advanced machine learning methods can be used as data-driven approaches for landslide susceptibility zonation with several landslide conditioning factors. Despite there being a number of studies on landslide susceptibility assessment, the literature is limited in several contexts, such as parameter optimization, an examination of the factors in detail, and study area. This study addresses these lacks in the literature and aims to develop a landslide susceptibility map of Kentucky, US. Four machine learning methods, namely artificial neural network (ANN), k-nearest neighbor (KNN), support vector machine (SVM), and stochastic gradient boosting (SGB), were used to train the dataset comprising sixteen landslide conditioning factors after pre-processing the data in terms of data encoding, data scaling, and dimension reduction. The hyperparameters of the machine learning methods were optimized using a state-of-the-art artificial bee colony (ABC) algorithm. The permutation importance and Shapley additive explanations (SHAP) methods were employed to reduce the dimension of the dataset and examine the contributions of each landslide conditioning factor to the output variable, respectively. The findings show that the ABC-SGB hybrid model achieved the highest prediction performance. The SHAP summary plot developed using the ABC-SGB model shows that intense precipitation, distance to faults, and slope were the most significant factors affecting landslide susceptibility. The SHAP analysis further underlines that increases in intense precipitation, distance to faults, and slope are associated with an increase in the probability of landslide incidents. The findings attained in this study can be used by decision makers to develop the most effective resource allocation plan for preventing landslides and minimizing related damages.

Suggested Citation

  • Tuba Bostan, 2024. "Generating a Landslide Susceptibility Map Using Integrated Meta-Heuristic Optimization and Machine Learning Models," Sustainability, MDPI, vol. 16(21), pages 1-27, October.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:21:p:9396-:d:1509337
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    References listed on IDEAS

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    1. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    2. Malcolm G. Anderson & Elizabeth Holcombe, 2013. "Community-Based Landslide Risk Reduction : Managing Disasters in Small Steps," World Bank Publications - Books, The World Bank Group, number 12239.
    3. Nour A. Mohamed & Hany M. Hasanien & Abdulaziz Alkuhayli & Tlenshiyeva Akmaral & Francisco Jurado & Ahmed O. Badr, 2023. "Hybrid Particle Swarm and Gravitational Search Algorithm-Based Optimal Fractional Order PID Control Scheme for Performance Enhancement of Offshore Wind Farms," Sustainability, MDPI, vol. 15(15), pages 1-25, August.
    4. Karpagam Sundararajan & Kathiravan Srinivasan, 2024. "A Synergistic Optimization Algorithm with Attribute and Instance Weighting Approach for Effective Drought Prediction in Tamil Nadu," Sustainability, MDPI, vol. 16(7), pages 1-24, April.
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