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Landslide Assessment Classification Using Deep Neural Networks Based on Climate and Geospatial Data

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  • Yadviga Tynchenko

    (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
    Laboratory of Biofuel Compositions, Siberian Federal University, 660041 Krasnoyarsk, Russia)

  • Vladislav Kukartsev

    (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
    Department of Information Economic Systems, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia)

  • Vadim Tynchenko

    (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
    Information-Control Systems Department, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia)

  • Oksana Kukartseva

    (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
    Laboratory of Biofuel Compositions, Siberian Federal University, 660041 Krasnoyarsk, Russia)

  • Tatyana Panfilova

    (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
    Department of Technological Machines and Equipment of Oil and Gas Complex, Siberian Federal University, 660041 Krasnoyarsk, Russia)

  • Alexey Gladkov

    (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia)

  • Van Nguyen

    (Institute of Energy and Mining Mechanical Engineering—Vinacomin, Hanoi 100000, Vietnam)

  • Ivan Malashin

    (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia)

Abstract

This study presents a method for classifying landslide triggers and sizes using climate and geospatial data. The landslide data were sourced from the Global Landslide Catalog (GLC), which identifies rainfall-triggered landslide events globally, regardless of size, impact, or location. Compiled from 2007 to 2018 at NASA Goddard Space Flight Center, the GLC includes various mass movements triggered by rainfall and other events. Climatic data for the 10 years preceding each landslide event, including variables such as rainfall amounts, humidity, pressure, and temperature, were integrated with the landslide data. This dataset was then used to classify landslide triggers and sizes using deep neural networks (DNNs) optimized through genetic algorithm (GA)-driven hyperparameter tuning. The optimized DNN models achieved accuracies of 0.67 and 0.82, respectively, in multiclass classification tasks. This research demonstrates the effectiveness of GA to enhance landslide disaster risk management.

Suggested Citation

  • Yadviga Tynchenko & Vladislav Kukartsev & Vadim Tynchenko & Oksana Kukartseva & Tatyana Panfilova & Alexey Gladkov & Van Nguyen & Ivan Malashin, 2024. "Landslide Assessment Classification Using Deep Neural Networks Based on Climate and Geospatial Data," Sustainability, MDPI, vol. 16(16), pages 1-26, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:7063-:d:1458401
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    References listed on IDEAS

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