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Open AccessArticle

Dam Site Suitability Mapping and Analysis Using an Integrated GIS and Machine Learning Approach

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Civil and Environmental Engineering Department, University of Sharjah, Sharjah 27272, UAE
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Research Institute of Sciences and Engineering, University of Sharjah, Sharjah 27272, UAE
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Department of Engineering, School of Engineering and Mathematical Sciences, La Trobe University, Melbourne, Victoria 3086, Australia
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Sharjah Electricity and Water Authority, Sharjah 135, UAE
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Author to whom correspondence should be addressed.
Water 2019, 11(9), 1880; https://doi.org/10.3390/w11091880
Received: 28 July 2019 / Revised: 28 August 2019 / Accepted: 4 September 2019 / Published: 10 September 2019
(This article belongs to the Section Water Resources Management and Governance)
: Meeting water demands is a critical pillar for sustaining normal human living standards, industry evolution and agricultural growth. The main obstacles for developing countries in arid regions include unplanned urbanisation and limited water resources. Locating and constructing dams is a strategic priority of countries to preserve and store water. Recent advances in remote sensing, geographic information system (GIS), and machine learning (ML) techniques provide valuable tools for producing a dam site suitability map (DSSM). In this research, a hybrid GIS decision-making technique supported by an ML algorithm was developed to identify the most appropriate location to construct a new dam for Sharjah, one of the major cities in the United Arab Emirates. Nine thematic layers have been considered to prepare the DSSM, including precipitation, drainage stream density, geomorphology, geology, curve number, total dissolved solid elevation, slope and major fracture. The weights of the thematic layers were determined through the analytical hierarchy process supported by several ML techniques, where the best attempted ML technique was the random forest method, with an accuracy of 76%. Precipitation and drainage stream density were the most influential factors affecting the DSSM. The developed DSSM was validated using existing dams across the study area, where the DSSM provides an accuracy of 83% for dams located in the high and moderate zones. Three major sites were identified as suitable locations for constructing new dams in Sharjah. The approach adopted in this study can be applied for any other location globally to identify potential dam construction sites.
Keywords: water scarcity; dam site suitability map; GIS; machine learning; analytical hierarchical process; Sharjah water scarcity; dam site suitability map; GIS; machine learning; analytical hierarchical process; Sharjah
MDPI and ACS Style

Al-Ruzouq, R.; Shanableh, A.; Yilmaz, A.G.; Idris, A.; Mukherjee, S.; Khalil, M.A.; Gibril, M.B.A. Dam Site Suitability Mapping and Analysis Using an Integrated GIS and Machine Learning Approach. Water 2019, 11, 1880.

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