Unraveling the complexities of urban fluvial flood hydraulics through AI

• Published in: Scientific reports Vol. 12; no. 1; p. 18738
• Main Authors: Mehedi, Md Abdullah Al, Smith, Virginia, Hosseiny, Hossein, Jiao, Xun
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 04.11.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 704/172; 704/242; Artificial Intelligence; Computer applications; Deep learning; Flooding; Floods; Humanities and Social Sciences; Hydraulic models; Hydraulics; multidisciplinary; Neural networks; Neural Networks, Computer; Outfalls; Science; Science (multidisciplinary); Stormwater; Stormwater management; Support Vector Machine; Urban areas; Urbanization; Water; Water depth

As urbanization increases across the globe, urban flooding is an ever-pressing concern. Urban fluvial systems are highly complex, depending on a myriad of interacting variables. Numerous hydraulic models are available for analyzing urban flooding; however, meeting the demand of high spatial extensio...