干旱区生态环境知识资源中心(Arid): Spatial mapping of artesian zone at Iraqi southern desert using a GIS-based random forest machine learning model

Arid

DOI	10.1007/s40808-016-0150-6
	Spatial mapping of artesian zone at Iraqi southern desert using a GIS-based random forest machine learning model
	Al-Abadi, Alaa M.; Shahid, Shamsuddin
通讯作者	Al-Abadi, AM
来源期刊	MODELING EARTH SYSTEMS AND ENVIRONMENT
ISSN	2363-6203
EISSN	2363-6211
出版年	2016
卷号	2 期号:2
英文摘要	Random forest (RF) machine learning technique and geographical information system (GIS) have been applied to delineate groundwater flowing well zones in the southern desert of Iraq. A spatial database consists of target variable, i.e., geographic locations of 93 flowing wells and predictor variables, i.e., the factors that control groundwater occurrence was prepared for this purpose. Eleven predictor variables were selected based on data availability, literature review, and field conditions which include elevation, slope, profile curvature, aspect, topographic wetness index, stream power index, distance to Abu Jir fault, distance to Euphrates River, major aquifer group, total hydraulic head, and well depth. The RF model in R package along with ArcGIS 10.2 was used to generate groundwater flowing well potential index for the study area. The obtained potential indices were classified using natural break classification scheme into five categories namely, very low, low, moderate, high, and very high. The results revealed that high or very high groundwater flowing well potential zones occupy 15 %, moderate potential zone covers 6 %, and low or very low potential zones cover 79 % of the southern desert of Iraq. The groundwater flowing well zone map was validated using relative operating characteristic (ROC) curve. The areas under the ROC curve for success and prediction rates were 0.98 and 0.97, respectively, indicating excellent capability of RF model to delineate groundwater potential. It is expected that the method development in this study can be used for rapid but efficient evaluation of groundwater flowing well potential from limited amount of data.
英文关键词	Random forest Groundwater Southern desert of Iraq ROC GIS
类型	Article
语种	英语
开放获取类型	Bronze
收录类别	ESCI
WOS记录号	WOS:000443087600047
WOS关键词	DECISION-ANALYSIS ; NEURAL-NETWORKS ; GROUNDWATER ; AREA ; PREDICTION ; ACCURACY ; WEIGHTS ; REGION ; BASIN ; TOOL
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/331999
作者单位	[Al-Abadi, Alaa M.] Univ Basra, Dept Geol, Coll Sci, Basra, Iraq; [Shahid, Shamsuddin] Univ Teknol Malaysia, Fac Civil Engn, Johor Baharu 81310, Malaysia
推荐引用方式 GB/T 7714	Al-Abadi, Alaa M.,Shahid, Shamsuddin. Spatial mapping of artesian zone at Iraqi southern desert using a GIS-based random forest machine learning model[J],2016,2(2).
APA	Al-Abadi, Alaa M.,&Shahid, Shamsuddin.(2016).Spatial mapping of artesian zone at Iraqi southern desert using a GIS-based random forest machine learning model.MODELING EARTH SYSTEMS AND ENVIRONMENT,2(2).
MLA	Al-Abadi, Alaa M.,et al."Spatial mapping of artesian zone at Iraqi southern desert using a GIS-based random forest machine learning model".MODELING EARTH SYSTEMS AND ENVIRONMENT 2.2(2016).