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Game-theoretic optimization of landslide susceptibility map**: a comparative study between Bayesian-optimized basic neural network and new generation neural network models

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Abstract

Landslide susceptibility map** is essential for reducing the risk of landslides and ensuring the safety of people and infrastructure in landslide-prone areas. However, little research has been done on the development of well-optimized Elman neural networks (ENN), deep neural networks (DNN), and artificial neural networks (ANN) for robust landslide susceptibility map** (LSM). Additionally, there is a research gap regarding the use of Bayesian optimization and the derivation of SHapley Additive exPlanations (SHAP) values from optimized models. Therefore, this study aims to optimize DNN, ENN, and ANN models using Bayesian optimization for landslide susceptibility map** and derive SHAP values from these optimized models. The LSM models have been validated using the receiver operating characteristics curve, confusion matrix, and other twelve error matrices. The study used six machine learning-based feature selection techniques to identify the most important variables for predicting landslide susceptibility. The decision tree, random forest, and bagging feature selection models showed that slope, elevation, DFR, annual rainfall, LD, DD, RD, and LULC are influential variables, while geology and soil texture have less influence. The DNN model outperformed the other two models, covering 7839.54 km2 under the very low landslide susceptibility zone and 3613.44 km2 under the very high landslide susceptibility zone. The DNN model is better suited for generating landslide susceptibility maps, as it can classify areas with higher accuracy. The model identified several key factors that contribute to the initiation of landslides, including high elevation, built-up and agricultural land use, less vegetation, aspect (north and northwest), soil depth less than 140 cm, high rainfall, high lineament density, and a low distance from roads. The study’s findings can help stakeholders make informed decisions to reduce the risk of landslides and ensure the safety of people and infrastructure in landslide-prone areas.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Research Group under grant no. R.G.P2/68/44. The authors are also thankful to the USGS Earth Explorer for making the LANDSAT data freely available.

Funding

Funding for this research was given under award no. R.G.P2/68/44 by the Deanship of Scientific Research; King Khalid University, Ministry of Education, Kingdom of Saudi Arabia.

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Authors and Affiliations

  1. Department of Civil Engineering, College of Engineering, King Khalid University, P.O. Box: 394, Abha, 61411, Kingdom of Saudi Arabia

    Javed Mallick & Meshel Alkahtani

  2. Department of Geography, Faculty of Natural Science, Jamia Millia Islamia, New Delhi, India

    Hoang Thi Hang

  3. Department of Energy and Environment, Analytical and Geochemistry Laboratory, TERI School of Advanced Studies, New Delhi, India

    Chander Kumar Singh

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  1. Javed Mallick
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Contributions

Conceptualization: Javed Mallick and Hoang Thi Hang; data curation: Javed Mallick and Hoang Thi Hang; formal analysis: Javed Mallick; funding acquisition: Javed Mallick; methodology: Javed Mallick and Meshel Alkahtani; project administration: Meshel Alkahtani and Chander Kumar Singh; resources: Meshel Alkahtani; software: Javed Mallick and Hoang Thi Hang; supervision: Javed Mallick; validation: Javed Mallick; writing—original draft: Javed Mallick; writing—review and editing: Chander Kumar Singh

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Correspondence to Javed Mallick.

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Mallick, ., Alkahtani, M., Hang, H.T. et al. Game-theoretic optimization of landslide susceptibility map**: a comparative study between Bayesian-optimized basic neural network and new generation neural network models. Environ Sci Pollut Res 31, 29811–29835 (2024). https://doi.org/10.1007/s11356-024-33128-w

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