Библиографическое описание:Flood Susceptibility Assessment in Urban Areas via Deep Neural Network Approach : научное издание / T. T. Panfilova [et al.]. - Текст : непосредственный // Sustainability. - 2024. - Т. 16, № 17. - ISSN 2071-1050, DOI 10.3390/su16177489.
Аннотация:<jats:p>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.</jats:p>
Держатель оригинала документа:Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia, Center for Continuing Education, Bauman Moscow State Technical University, 105005 Moscow, Russia, Department of Information Economic Systems, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia, Department of Technological Machines and Equipment of Oil and Gas Complex, Siberian Federal University, 660041 Krasnoyarsk, Russia, Information-Control Systems Department, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia, Laboratory of Biofuel Compositions, Siberian Federal University, 660041 Krasnoyarsk, Russia, School of Electrical Engineering, Hebei University of Technology, Tianjin 300401, China
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