Flood Susceptibility Assessment in Urban Areas via Deep Neural Network Approach

Tatyana Panfilova, Vladislav Kukartsev, Vadim Tynchenko (), Yadviga Tynchenko, Oksana Kukartseva, Ilya Kleshko, Xiaogang Wu and Ivan Malashin ()
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Tatyana Panfilova: Center for Continuing Education, Bauman Moscow State Technical University, 105005 Moscow, Russia
Vladislav Kukartsev: Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
Vadim Tynchenko: Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
Yadviga Tynchenko: Center for Continuing Education, Bauman Moscow State Technical University, 105005 Moscow, Russia
Oksana Kukartseva: Center for Continuing Education, Bauman Moscow State Technical University, 105005 Moscow, 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

Sustainability, 2024, vol. 16, issue 17, 1-23

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.

Keywords: multiclass classification; floods; sustainable urban development; disaster risk reduction; sustainable cities and communities; urban environment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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