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Multilayer Perceptron and Their Comparison with Two Nature-Inspired Hybrid Techniques of Biogeography-Based Optimization (BBO) and Backtracking Search Algorithm (BSA) for Assessment of Landslide Susceptibility

Author

Listed:
  • Hossein Moayedi

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
    School of Engineering and Technology, Duy Tan University, Da Nang 550000, Vietnam)

  • Peren Jerfi Canatalay

    (Department of Computer Engineering, Faculty of Engineering, Haliç University, Istanbul 34394, Turkey)

  • Atefeh Ahmadi Dehrashid

    (Department of Climatology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 6617715175, Iran
    Member of Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan, Sanandaj 6617715175, Iran)

  • Mehmet Akif Cifci

    (Department of Computer Engineering, Bandirma Onyedi Eylul University, Balikesir 10200, Turkey)

  • Marjan Salari

    (Department of Civil Engineering, Sirjan University of Technology, Sirjan 7813733385, Iran)

  • Binh Nguyen Le

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
    School of Engineering and Technology, Duy Tan University, Da Nang 550000, Vietnam)

Abstract

Regarding evaluating disaster risks in Iran’s West Kurdistan area, the multi-layer perceptron (MLP) neural network was upgraded with two novel techniques: backtracking search algorithm (BSA) and biogeography-based optimization (BBO). Utilizing 16 landslide conditioning elements such as elevation (aspect), plan (curve), profile (curvature), geology, NDVI (land use), slope (degree), stream power index (SPI), topographic wetness index (TWI), rainfall, and sediment transport index (STI), and 504 landslides as target variables, a large geographic database is constructed. Applying the techniques mentioned above to the synthesis of the MLP results in the suggested BBO-MLP and BSA-MLP ensembles. As accuracy standards, we benefit from mean absolute error, mean square error, and area under the receiving operating characteristic curve to assess the utilized models, we have also designed a scoring system. The MLP’s accuracy increases thanks to the application of the BBO and BSA algorithms. Comparing the BBO with the BSA, we find that the former achieves higher average MLP optimization ranks (20, 15, and 14). A further finding showed that the BBO is superior to the BSA at maximizing the MLP.

Suggested Citation

  • Hossein Moayedi & Peren Jerfi Canatalay & Atefeh Ahmadi Dehrashid & Mehmet Akif Cifci & Marjan Salari & Binh Nguyen Le, 2023. "Multilayer Perceptron and Their Comparison with Two Nature-Inspired Hybrid Techniques of Biogeography-Based Optimization (BBO) and Backtracking Search Algorithm (BSA) for Assessment of Landslide Susce," Land, MDPI, vol. 12(1), pages 1-25, January.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:1:p:242-:d:1033793
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    References listed on IDEAS

    as
    1. Yinghao Zhao & Hesong Hu & Lunhua Bai & Mengxiong Tang & Hang Chen & Dingli Su, 2021. "Fragility Analyses of Bridge Structures Using the Logarithmic Piecewise Function-Based Probabilistic Seismic Demand Model," Sustainability, MDPI, vol. 13(14), pages 1-23, July.
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