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Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm
Authors:Mohamadi  Sedigheh  Sammen  Saad Sh.  Panahi  Fatemeh  Ehteram  Mohammad  Kisi  Ozgur  Mosavi  Amir  Ahmed  Ali Najah  El-Shafie  Ahmed  Al-Ansari  Nadhir
Affiliation:1.Department of Ecology, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran
;2.Department of Civil Engineering, College of Engineering, University of Diyala, Baqubah, Diyala Governorate, Iraq
;3.Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran
;4.Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran
;5.Department of Civil Engineering, School of Technology, IIia State University, 0162, Tbilisi, Georgia
;6.Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam
;7.Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam
;8.Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam
;9.Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), 43000, Kajang, Selangor Darul Ehsan, Malaysia
;10.Department of Civil Engineering, Faculty of Engineering, University of Malaya (UM), 50603, Kuala Lumpur, Malaysia
;11.National Water Center (NWC), United Arab Emirates University, P.O. Box 15551, Al Ain, UAE
;12.Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187, Lule?, Sweden
;
Abstract:Natural Hazards - The modelling of drought is of utmost importance for the efficient management of water resources. This article used the adaptive neuro-fuzzy interface system (ANFIS), multilayer...
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