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Flood Susceptibility Assessment in Urban Areas via Deep Neural Network Approach

Author

Listed:
  • Tatyana Panfilova

    (Center for Continuing Education, 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)

  • 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)

  • Yadviga Tynchenko

    (Center for Continuing Education, Bauman Moscow State Technical University, 105005 Moscow, Russia
    Laboratory of Biofuel Compositions, Siberian Federal University, 660041 Krasnoyarsk, Russia)

  • Oksana Kukartseva

    (Center for Continuing Education, Bauman Moscow State Technical University, 105005 Moscow, Russia
    Laboratory of Biofuel Compositions, Siberian Federal University, 660041 Krasnoyarsk, Russia)

  • Ilya Kleshko

    (Center for Continuing Education, Bauman Moscow State Technical University, 105005 Moscow, Russia)

  • Xiaogang Wu

    (School of Electrical Engineering, Hebei University of Technology, Tianjin 300401, China)

  • Ivan Malashin

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

Abstract

Floods, caused by intense rainfall or typhoons, overwhelming urban drainage systems, pose significant threats to urban areas, leading to substantial economic losses and endangering human lives. This study proposes a methodology for flood assessment in urban areas using a multiclass classification approach with a Deep Neural Network (DNN) optimized through hyperparameter tuning with genetic algorithms (GAs) leveraging remote sensing data of a flood dataset for the Ibadan metropolis, Nigeria and Metro Manila, Philippines. The results show that the optimized DNN model significantly improves flood risk assessment accuracy (Ibadan-0.98) compared to datasets containing only location and precipitation data (Manila-0.38). By incorporating soil data into the model, as well as reducing the number of classes, it is able to predict flood risks more accurately, providing insights for proactive flood mitigation strategies and urban planning.

Suggested Citation

  • Tatyana Panfilova & Vladislav Kukartsev & Vadim Tynchenko & Yadviga Tynchenko & Oksana Kukartseva & Ilya Kleshko & Xiaogang Wu & Ivan Malashin, 2024. "Flood Susceptibility Assessment in Urban Areas via Deep Neural Network Approach," Sustainability, MDPI, vol. 16(17), pages 1-23, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7489-:d:1467082
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    References listed on IDEAS

    as
    1. Abdulwaheed Tella & Abdul-Lateef Balogun, 2020. "Ensemble fuzzy MCDM for spatial assessment of flood susceptibility in Ibadan, Nigeria," 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. 104(3), pages 2277-2306, December.
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