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Comparison of optimized data-driven models for landslide susceptibility mapping

Author

Listed:
  • Armin Ghayur Sadigh

    (K. N. Toosi University of Technology)

  • Ali Asghar Alesheikh

    (K. N. Toosi University of Technology)

  • Sayed M. Bateni

    (University of Hawaii at Manoa)

  • Changhyun Jun

    (Chung-Ang University)

  • Saro Lee

    (Geoscience Data Center, Korea Institute of Geoscience and Mineral Resources (KIGAM)
    Korea University of Science and Technology)

  • Jeffrey R. Nielson

    (Washington State University)

  • Mahdi Panahi

    (Stockholm University
    Chosun University)

  • Fatemeh Rezaie

    (Geoscience Data Center, Korea Institute of Geoscience and Mineral Resources (KIGAM)
    Korea University of Science and Technology)

Abstract

Locations prone to landslides must be identified and mapped to prevent landslide-related damage and casualties. Machine learning approaches have proven effective for such tasks and have thus been widely applied. However, owing to the rapid development of data-driven approaches, deep learning methods that can exhibit enhanced prediction accuracies have not been fully evaluated. Several researchers have compared different methods without optimizing them, whereas others optimized a single method using different algorithms and compared them. In this study, the performances of different fully optimized methods for landslide susceptibility mapping within the landslide-prone Kermanshah province of Iran were compared. The models, i.e., convolutional neural networks (CNNs), deep neural networks (DNNs), and support vector machine (SVM) frameworks were developed using 14 conditioning factors and a landslide inventory containing 110 historical landslide points. The models were optimized to maximize the area under the receiver operating characteristic curve (AUC), while maintaining their stability. The results showed that the CNN (accuracy = 0.88, root mean square error (RMSE) = 0.37220, and AUC = 0.88) outperformed the DNN (accuracy = 0.79, RMSE = 0.40364, and AUC = 0.82) and SVM (accuracy = 0.80, RMSE = 0.42827, and AUC = 0.80) models using the same testing dataset. Moreover, the CNN model exhibiting the highest robustness among the three models, given its smallest AUC difference between the training and testing datasets. Notably, the dataset used in this study had a low spatial accuracy and limited sample points, and thus, the CNN approach can be considered useful for susceptibility assessment in other landslide-prone regions worldwide, particularly areas with poor data quality and quantity. The most important conditioning factors for all models were rainfall and the distances from roads and drainages.

Suggested Citation

  • Armin Ghayur Sadigh & Ali Asghar Alesheikh & Sayed M. Bateni & Changhyun Jun & Saro Lee & Jeffrey R. Nielson & Mahdi Panahi & Fatemeh Rezaie, 2024. "Comparison of optimized data-driven models for landslide susceptibility mapping," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(6), pages 14665-14692, June.
  • Handle: RePEc:spr:endesu:v:26:y:2024:i:6:d:10.1007_s10668-023-03212-1
    DOI: 10.1007/s10668-023-03212-1
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    References listed on IDEAS

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    1. Paul Sestraș & Ștefan Bilașco & Sanda Roșca & Sanda Naș & Mircea V. Bondrea & Raluca Gâlgău & Ioel Vereș & Tudor Sălăgean & Velibor Spalević & Sorin M. Cîmpeanu, 2019. "Landslides Susceptibility Assessment Based on GIS Statistical Bivariate Analysis in the Hills Surrounding a Metropolitan Area," Sustainability, MDPI, vol. 11(5), pages 1-23, March.
    2. Ujjwal Sur & Prafull Singh & Praveen Kumar Rai & Jay Krishna Thakur, 2021. "Landslide probability mapping by considering fuzzy numerical risk factor (FNRF) and landscape change for road corridor of Uttarakhand, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(9), pages 13526-13554, September.
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