Knowledge Resource Center for Ecological Environment in Arid Area
| DOI | 10.3390/w11091880 |
| Dam Site Suitability Mapping and Analysis Using an Integrated GIS and Machine Learning Approach | |
| Al-Ruzouq, Rami1,2; Shanableh, Abdallah1,2; Yilmaz, Abdullah Gokhan1,2,3; Idris, AlaEldin4; Mukherjee, Sunanda2; Khalil, Mohamad Ali2; Gibril, Mohamed Barakat A.2 | |
| 通讯作者 | Al-Ruzouq, Rami |
| 来源期刊 | WATER
 |
| EISSN | 2073-4441 |
| 出版年 | 2019 |
| 卷号 | 11期号:9 |
| 英文摘要 | 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. |
| 英文关键词 | water scarcity dam site suitability map GIS machine learning analytical hierarchical process Sharjah |
| 类型 | Article |
| 语种 | 英语 |
| 国家 | U Arab Emirates ; Australia |
| 开放获取类型 | gold |
| 收录类别 | SCI-E |
| WOS记录号 | WOS:000488834400143 |
| WOS关键词 | MODELING OPTIMUM SITES ; WATER-RESOURCES ; SUITABLE SITES |
| WOS类目 | Environmental Sciences ; Water Resources |
| WOS研究方向 | Environmental Sciences & Ecology ; Water Resources |
| 资源类型 | 期刊论文 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/219247 |
| 作者单位 | 1.Univ Sharjah, Civil & Environm Engn Dept, Sharjah 27272, U Arab Emirates; 2.Univ Sharjah, Res Inst Sci & Engn, Sharjah 27272, U Arab Emirates; 3.La Trobe Univ, Sch Engn & Math Sci, Dept Engn, Melbourne, Vic 3086, Australia; 4.Sharjah Elect & Water Author, Sharjah 135, U Arab Emirates |
| 推荐引用方式 GB/T 7714 | Al-Ruzouq, Rami,Shanableh, Abdallah,Yilmaz, Abdullah Gokhan,et al. Dam Site Suitability Mapping and Analysis Using an Integrated GIS and Machine Learning Approach[J],2019,11(9). |
| APA | Al-Ruzouq, Rami.,Shanableh, Abdallah.,Yilmaz, Abdullah Gokhan.,Idris, AlaEldin.,Mukherjee, Sunanda.,...&Gibril, Mohamed Barakat A..(2019).Dam Site Suitability Mapping and Analysis Using an Integrated GIS and Machine Learning Approach.WATER,11(9). |
| MLA | Al-Ruzouq, Rami,et al."Dam Site Suitability Mapping and Analysis Using an Integrated GIS and Machine Learning Approach".WATER 11.9(2019). |
| 条目包含的文件 | 条目无相关文件。 | |||||
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