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Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster |
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| Authors: | Afan Haitham Abdulmohsin Yafouz Ayman Birima Ahmed H. Ahmed Ali Najah Kisi Ozgur Chaplot Barkha El-Shafie Ahmed |
| Affiliation: | 1.Department of Civil Engineering, Al-maarif University College, Ramadi, Iraq ;2.Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Kajang 43000, Selangor Darul Ehsan, Malaysia ;3.Department of Civil Engineering, College of Engineering, Qassim University, Unaizah, Saudi Arabia ;4.Institute for Energy Infrastructure (IEI), Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Selangor, Malaysia ;5.Civil Engineering Department, Ilia State University, Tbilisi, Georgia ;6.Department of Geography, M.J.K. College, Bettiah, A Constituent unit of Babasaheb Bhimrao Ambedkar Bihar University, Muzaffarpur, Bihar, India ;7.Department of Civil Engineering, Faculty of Engineering, University of Malaya (UM), 50603, Kuala Lumpur, Malaysia ;8.National Water and Energy Center, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates ; |
| Abstract: | Natural Hazards - Due to the need to reduce the flooding disaster, river streamflow prediction is required to be enhanced by the aid of deep learning algorithms. To achieve accurate model of... |
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