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Evaluating the influence of road construction on landslide susceptibility in Saudi Arabia’s mountainous terrain: a Bayesian-optimised deep learning approach with attention mechanism and sensitivity analysis

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Abstract

In the mountainous region of Asir region of Saudi Arabia, road construction activities are closely associated with frequent landslides, posing significant risks to both human life and infrastructural development. This highlights an urgent need for a highly accurate landslide susceptibility map to guide future development and risk mitigation strategies. Therefore, this study aims to (1) develop robust well-optimised deep learning (DL) models for predicting landslide susceptibility and (2) conduct a comprehensive sensitivity analysis to quantify the impact of each parameter influencing landslides. To achieve these aims, three advanced DL models—Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Bayesian-optimised CNN with an attention mechanism—were rigorously trained and validated. Model validation included eight matrices, calibration curves, and Receiver Operating Characteristic (ROC) and Precision-Recall curves. Multicollinearity was examined using Variance Inflation Factor (VIF) to ensure variable independence. Additionally, sensitivity analysis was used to interpret the models and explore the influence of parameters on landslide. Results showed that road networks significantly influenced the areas identified as high-risk zones. Specifically, in the 1-km buffer around roadways, CNN_AM identified 10.42% of the area as ‘Very High’ susceptibility—more than double the 4.04% indicated by DNN. In the extended 2-km buffer zone around roadways, Bayesian CNN_AM continued to flag a larger area as Very High risk (7.46%), in contrast to DNN’s 3.07%. In performance metrics, CNN_AM outshined DNN and regular CNN models, achieving near-perfect scores in Area Under the Curve (AUC), precision-recall, and overall accuracy. Sensitivity analysis highlighted ‘Soil Texture’, ‘Geology’, ‘Distance to Road’, and ‘Slope’ as crucial for landslide prediction. This research offers a robust, high-accuracy model that emphasises the role of road networks in landslide susceptibility, thereby providing valuable insights for planners and policymakers to proactively mitigate landslide risks in vulnerable zones near existing and future road infrastructure.

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Data availability

The datasets used and/or analysed 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 number R.G.P.2/442/44. Authors also thankful to the undergraduate students of College of Engineering, King Khalid University for assisting in data collections.

Funding

Funding for this research was given under award number RGP2/442/44 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

    Saeed Alqadhi, Javed Mallick & Abdullah Faiz Saeed Al Asmari

  2. Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, 110025, India

    Hoang Thi Hang

  3. School of Environment and Sustainable Development (SESD), Central University of Gujarat, Gujarat, India

    Rina Kumari

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  1. Saeed Alqadhi
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Contributions

Conceptualisation, Saeed Alqadhi, Javed Mallick, Hoang Thi Hang, and Abdullah Faiz Saeed Al Asmari; data curation and formal analysis, Javed Mallick, Saeed Alqadhi, Hoang Thi Hang, and Abdullah Faiz Saeed Al Asmari; funding acquisition, Saeed Alqadhi; methodology, Javed Mallick, Hoang Thi Hang, Abdullah Faiz Saeed Al Asmari, and Rina Kumari; project administration, Saeed Alqadhi and Javed Mallick; resources, Hoang Thi Hang, Abdullah Faiz Saeed Al Asmari, and Rina Kumari; software, Javed Mallick; supervision, Javed Mallick and Rina Kumari; validation: Saeed Alqadhi and Javed Mallick; writing — original draft, Saeed Alqadhi and Javed Mallick Swapan Talukdar; writing — review and editing, Hoang Thi Hang, Abdullah Faiz Saeed Al Asmari, and Rina Kumari.

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Correspondence to Javed Mallick.

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Alqadhi, S., Mallick, J., Hang, H.T. et al. Evaluating the influence of road construction on landslide susceptibility in Saudi Arabia’s mountainous terrain: a Bayesian-optimised deep learning approach with attention mechanism and sensitivity analysis. Environ Sci Pollut Res 31, 3169–3194 (2024). https://doi.org/10.1007/s11356-023-31352-4

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