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Comparative analysis of multiple conventional neural networks for landslide susceptibility mapping

Author

Listed:
  • Bilal Aslam

    (Riphah International University
    Northern Arizona University)

  • Adeel Zafar

    (Riphah International University)

  • Umer Khalil

    (University of Twente)

Abstract

In landslide susceptible mountainous regions, the precondition for avoiding and alleviating perilous dangers is the susceptibility mapping of the landslide. In northern Pakistan, landslides due to vigorous seismic zones, monsoon rainfall, extremely sheer slopes, and unfavorable geological conditions present a considerable threat to the mountain areas. This study targets and advances the research in mapping landslide susceptibility in northern Pakistan (Mansehra and Muzaffarabad districts). The central objective of the analysis is to analyze different convolutional neural network (CNN) frameworks and residual network (ResNet) that were constructed by developing distinct data representation algorithms for landslide susceptibility assessment and compare the results. This study considers sixteen landslide conditioning factors related to the incident of landslides centered on the literature review and geologic attributes of the pondered area. The marked historical landslide positions in the deliberated area were arbitrarily split into training and testing datasets, with the earlier containing 70% and the former having 30% of the total datasets. Several commonly exploited measures were used to validate the CNN architectures and ResNet by comparing them with the most prevalent machine learning (ML) and deep learning (DL) techniques. The outcomes of this study revealed that the proportions of regions having very high susceptibility in all the landslide susceptibility maps of the ResNet model and CNN models are considerably alike and less than 20%, which implies that the CNN models are significantly helpful in managing and preventing landslides as to the orthodox techniques. Moreover, the suggested CNN architectures and ResNet attained greater or similar prediction accuracy than other orthodox ML and DL techniques. The values of OA (overall accuracy) and MCC (Matthew’s correlation coefficient) of proposed CNNs and ResNet were greater than those of the optimized SVM (support vector machine) and DNN (deep neural network).

Suggested Citation

  • Bilal Aslam & Adeel Zafar & Umer Khalil, 2023. "Comparative analysis of multiple conventional neural networks for landslide susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(1), pages 673-707, January.
  • Handle: RePEc:spr:nathaz:v:115:y:2023:i:1:d:10.1007_s11069-022-05570-x
    DOI: 10.1007/s11069-022-05570-x
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    References listed on IDEAS

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    1. D. Kanungo & S. Sarkar & Shaifaly Sharma, 2011. "Combining neural network with fuzzy, certainty factor and likelihood ratio concepts for spatial prediction of landslides," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 59(3), pages 1491-1512, December.
    2. Christos Polykretis & Christos Chalkias, 2018. "Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 93(1), pages 249-274, August.
    3. Nefeslioglu, Hakan A. & Gorum, Tolga, 2020. "The use of landslide hazard maps to determine mitigation priorities in a dam reservoir and its protection area," Land Use Policy, Elsevier, vol. 91(C).
    4. Jewgenij Torizin & Michael Fuchs & Adnan Alam Awan & Ijaz Ahmad & Sardar Saeed Akhtar & Simon Sadiq & Asif Razzak & Daniel Weggenmann & Faseeh Fawad & Nimra Khalid & Faisan Sabir & Ahsan Jamal Khan, 2017. "Statistical landslide susceptibility assessment of the Mansehra and Torghar districts, Khyber Pakhtunkhwa Province, Pakistan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(2), pages 757-784, November.
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