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Estimating landslide hazard distribution based on machine learning and bivariate statistics in Utmah Region, Yemen

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Abstract

Landslides represent significant risks to human activity, leading to infrastructure damage and loss of life. This study focuses on assessing landslide hazards in Utmah Region, Yemen. The evaluation involves comparing the effectiveness of the relative frequency ratio model with five machine learning algorithms (MLAs) for hazard mapping. Field surveys, high-resolution satellite imagery, and aerial photography were utilized in the study. The inventory map was generated after identifying and mapping 100 landslides. The inventory was then divided randomly into a training dataset (70 landslides) and a validation dataset (30 landslides), with an equal number of non-landslide pixels. Eleven additional landslide conditioning factors were collected from various sources, and the frequency ratio (FR) approach was employed to identify the most crucial variables for modeling. The models were rigorously tested and assessed using statistical metrics, including the Friedman and Wilcoxon signed-rank tests, as well as the area under the receiver operating characteristics (AUROC) curve. The findings based on the training and validation datasets revealed that the RF algorithm (AUROC, 0.992) outperformed the other models in generating hazard maps. The XGBoost model (AUROC, 0.991), NB model (AUROC, 0.970), ANN model (AUROC, 0.922), KNN model (AUROC, 0.877), and FR (AUROC, 0.674) were found to be less effective. Consequently, the study highlights that the Random forest (RF) model shows promise as an effective approach for predicting landslides spatially on a global scale.

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Funding

This research was supported by the Major Scientific and Technological Program of Jilin Province (Grant No. 20200503002SF), the Science and Technology Development Planning of Jilin Province (Grant No. 20190303081SF), and the National Key R&D Program of China (2018YFC1508804). We express our gratitude to Dr. Omar Althuwaynee, who contribute to model design and the Scientists Adoption Academy (scadacademy.com) as a facilitator’s website for research development through discussions with professionals in the fields.

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Authors and Affiliations

  1. Institute of Natural Disaster Research, School of Environment, Northeast Normal University, Changchun, 130024, People’s Republic of China

    Yasser M. Khalil, Bazel Al-Shaibah & Xingpeng Liu

  2. Key Laboratory for Vegetation Ecology, Ministry of Education, Changchun, 130024, People’s Republic of China

    Xingpeng Liu

  3. State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, Northeast Normal University, Changchun, 130024, People’s Republic of China

    Xingpeng Liu

  4. Key Laboratory of GIS and RS Application Xinjiang Uygur Autonomous Region, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China

    Yousef A. Al-Masnay

  5. University of Chinese Academy of Sciences, Beijing, 100049, China

    Yousef A. Al-Masnay

  6. Department of Geology and Environment, Thamar University, Thamar, Yemen

    Nabil M. Al-Areeq

  7. Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin, 300072, China

    Ali R. Al-Aizari

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  1. Yasser M. Khalil
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Khalil, Y.M., Al-Masnay, Y.A., Al-Areeq, N.M. et al. Estimating landslide hazard distribution based on machine learning and bivariate statistics in Utmah Region, Yemen. Nat Hazards 120, 2869–2907 (2024). https://doi.org/10.1007/s11069-023-06310-5

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