Urban flood modeling using deep-learning approaches in Seoul, South Korea

Author:

Lei Xinxiang,Chen Wei,Panahi Mahdi,Falah Fatemeh,Rahmati Omid,Uuemaa Evelyn,Kalantari Zahra,Ferreira Carla Sofia Santos,Rezaie Fatemeh,Tiefenbacher John P.,Lee Saro,Bian Huiyuan

Funder

Korea Institute of Geoscience and Mineral Resources

Korea Ministry of Science and ICT

Ministry of Science, ICT and Future Planning

Publisher

Elsevier BV

Subject

Water Science and Technology

Reference98 articles.

1. Sensitivity of urban flood simulations to stormwater infrastructure and soil infiltration;Hossain Anni;J. Hydrol.,2020

2. Contribution of land use changes to future flood damage along the river Meuse in the Walloon region;Beckers;Nat. Hazards Earth Syst. Sci.,2013

3. Learning long-term dependencies with gradient descent is difficult;Bengio;IEEE Trans. Neural Networks,1994

4. A physically based, variable contributing area model of basin hydrology;Beven;Hydrol. Sci. Bull.,1979

5. Principles of geographical information systems;Burrough,1998

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