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Hydrological Sciences Journal Volume 65, 2020 - Issue 16
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Research Article

New neural fuzzy-based machine learning ensemble for enhancing the prediction accuracy of flood susceptibility mapping

Romulus Costachea Research Institute of the University of Bucharest, University of Bucharest, Bucharest, Romania;b Flash-Flood Forecast and Warning Department, National Institute of Hydrology and Water Management, Bucharest, RomaniaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-6876-8572
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Roxana Țîncuc Dimitrie Ghika Technical College, RomaniaORCID Iconhttps://orcid.org/0000-0001-6961-8464
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Ismail Elkhrachyd College of Engineering, Civil Engineering Department, Najran University, Najran, Saudi Arabia;e Faculty of Engineering, Civil Engineering Department, Al-Azhar University, Nasr City, Cairo, EgyptORCID Iconhttps://orcid.org/0000-0001-9766-779X
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Quoc Bao Phamf Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam;g Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, VietnamORCID Iconhttps://orcid.org/0000-0002-0468-5962
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Mihnea Cristian Popah Centre for Integrated Analysis and Territorial Management, University of Bucharest, Bucharest, Romania;i Simion Mehedinți–Nature and Sustainable Development Doctoral School, University of Bucharest, Bucharest, RomaniaORCID Iconhttps://orcid.org/0000-0002-7152-1714
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Daniel Constantin Diaconuh Centre for Integrated Analysis and Territorial Management, University of Bucharest, Bucharest, Romania;j Department of Meteorology and Hydrology, Faculty of Geography, University of Bucharest, Bucharest, RomaniaORCID Iconhttps://orcid.org/0000-0002-0468-6703
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Mohammadtaghi Avandk Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Tehran, IranORCID Iconhttps://orcid.org/0000-0001-7196-5051
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Iulia Costachel Faculty of Geography, University of Bucharest, Bucharest, RomaniaORCID Iconhttps://orcid.org/0000-0002-1779-8173
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Alireza Arabamerim Department of Geomorphology, Tarbiat Modares University, Tehran, IranORCID Iconhttps://orcid.org/0000-0002-1142-1666
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Dieu Tien Buin Institute of Research and Development, Duy Tan University, Da Nang, VietnamORCID Iconhttps://orcid.org/0000-0001-5161-6479
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Pages 2816-2837 | Received 23 Dec 2019, Accepted 20 Jul 2020, Published online: 20 Nov 2020

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Rahma Khalid & Usman T. Khan. (2024) Flood susceptibility mapping using ANNs: a case study in model generalization and accuracy from Ontario, Canada. Geocarto International 39:1.
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Javed Mallick, Mohammed K. Almesfer, Majed Alsubih, Swapan Talukdar, Mohd Ahmed & Nabil Ben Kahla. (2022) Developing a new method for future groundwater potentiality mapping under climate change in Bisha watershed, Saudi Arabia. Geocarto International 37:26, pages 14495-14527.
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Huu Duy Nguyen, Dinh Kha Dang, Quoc-Huy Nguyen, Quang-Thanh Bui & Alexandru-Ionut Petrisor. (2022) Evaluating the effects of climate and land use change on the future flood susceptibility in the central region of Vietnam by integrating land change modeler, machine learning methods. Geocarto International 37:26, pages 12810-12845.
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Bappa Sarkar, Prasanta Das, Nazrul Islam, Amiya Basak, Manoj Debnath & Ranjan Roy. (2022) Land suitability analysis for paddy crop using GIS-based Fuzzy-AHP (F-AHP) method in Koch Bihar district, West Bengal. Geocarto International 37:25, pages 8952-8978.
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Huu Duy Nguyen, Quoc-Huy Nguyen, Quan Vu Viet Du, Thi Ha Thanh Nguyen, Tien Giang Nguyen & Quang-Thanh Bui. (2022) A novel combination of deep neural network and Manta ray foraging optimization for flood susceptibility mapping in Quang Ngai province, Vietnam. Geocarto International 37:25, pages 7531-7555.
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Huu Duy Nguyen, Bui Quang-Thanh, Quoc-Huy Nguyen, Tien Giang Nguyen, Le Tuan Pham, Xuan Linh Nguyen, Phuong Lan Vu, Thi Ha Thanh Nguyen, Anh Tuan Nguyen & Alexandru-Ionut Petrisor. (2022) A novel hybrid approach to flood susceptibility assessment based on machine learning and land use change. Case study: a river watershed in Vietnam. Hydrological Sciences Journal 67:7, pages 1065-1083.
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