Тип публикации: статья из журнала

Год издания: 2024

Идентификатор DOI: 10.3390/su16177489

Ключевые слова: multiclass classification, floods, sustainable urban development, disaster risk reduction, sustainable cities and communities, urban environment

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 NeuralПоказать полностьюNetwork (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>

Журнал: Sustainability

Выпуск журнала: Т. 16, № 17

ISSN журнала: 20711050

• Panfilova Tatyana (Department of Technological Machines and Equipment of Oil and Gas Complex, Siberian Federal University, 660041 Krasnoyarsk, Russia)
• Kukartsev Vladislav (Department of Information Economic Systems, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia)
• Tynchenko Vadim (Information-Control Systems Department, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia)
• Tynchenko Yadviga (Laboratory of Biofuel Compositions, Siberian Federal University, 660041 Krasnoyarsk, Russia)
• Kukartseva Oksana (Laboratory of Biofuel Compositions, Siberian Federal University, 660041 Krasnoyarsk, Russia)
• Kleshko Ilya (Center for Continuing Education, Bauman Moscow State Technical University, 105005 Moscow, Russia)
• Wu Xiaogang (School of Electrical Engineering, Hebei University of Technology, Tianjin 300401, China)
• Malashin Ivan (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia)

• Ядро РИНЦ (eLIBRARY.RU)