干旱区生态环境知识资源中心(Arid): Groundwater potential mapping using a novel data-mining ensemble model

Arid

DOI	10.1007/s10040-018-1848-5
	Groundwater potential mapping using a novel data-mining ensemble model
	Kordestani, Mojtaba Dolat 1; Naghibi, Seyed Amir 2,3; Hashemi, Hossein 2,3; Ahmadi, Kourosh 4; Kalantar, Bahareh 5; Pradhan, Biswajeet 6,7
通讯作者	Naghibi, Seyed Amir
来源期刊	HYDROGEOLOGY JOURNAL
ISSN	1431-2174
EISSN	1435-0157
出版年	2019
卷号	27 期号:1 页码:211-224
英文摘要	Freshwater scarcity is an ever-increasing problem throughout the arid and semi-arid countries, and it often results in poverty. Thus, it is necessary to enhance understanding of freshwater resources availability, particularly for groundwater, and to be able to implement functional water resources plans. This study introduces a novel statistical approach combined with a data-mining ensemble model, through implementing evidential belief function and boosted regression tree (EBF-BRT) algorithms for groundwater potential mapping of the Lordegan aquifer in central Iran. To do so, spring locations are determined and partitioned into two groups for training and validating the individual and ensemble methods. In the next step, 12 groundwater-conditioning factors (GCFs), including topographical and hydrogeological factors, are prepared for the modeling process. The mentioned factors are employed in the application of the EBF model. Then, the EBF values of the GCFs are implemented as input to the BRT algorithm. The results of the modeling process are plotted to produce spring (groundwater) potential maps. To verify the results, the receiver operating characteristics (ROC) test is applied to the model's output. The findings of the test indicated that the areas under the ROC curves are 75 and 82% for the EBF and EBF-BRT models, respectively. Therefore, it can be inferred that the combination of the two techniques could increase the efficacy of these methods in groundwater potential mapping.
英文关键词	Geographic information system (GIS) Groundwater management Data mining Iran
类型	Article
语种	英语
国家	Iran ; Sweden ; Japan ; Australia ; South Korea
开放获取类型	hybrid, Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000458520300014
WOS关键词	SUPPORT VECTOR MACHINE ; ARTIFICIAL NEURAL-NETWORKS ; FREQUENCY RATIO ; SPATIAL PREDICTION ; RANDOM FOREST ; GIS ; BIVARIATE ; PROVINCE ; WEIGHTS ; REGION
WOS类目	Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/216162
作者单位	1.Jiroft Univ, Jiroft Univ Scholarship, Univ Hormozgan, Fac Rangeland & Watershed Management,Dept Combat, Jiroft, Iran; 2.Lund Univ, Ctr Middle Eastern Studies, Lund, Sweden; 3.Lund Univ, Dept Water Resources Engn, Lund, Sweden; 4.Tarbiat Modares Univ, Dept Forestry, Coll Nat Resources, Noor, Mazandaran, Iran; 5.RIKEN, Goal Oriented Technol Res Grp, Disaster Resilience Sci Team, Ctr Adv Intelligence Project, Tokyo 1030027, Japan; 6.Univ Technol Sydney, Fac Engn & IT, CAMGIS, Sydney, NSW 2007, Australia; 7.Sejong Univ, Dept Energy & Mineral Resources Engn, 209 Neungdong Ro, Seoul 05006, South Korea
推荐引用方式 GB/T 7714	Kordestani, Mojtaba Dolat,Naghibi, Seyed Amir,Hashemi, Hossein,et al. Groundwater potential mapping using a novel data-mining ensemble model[J],2019,27(1):211-224.
APA	Kordestani, Mojtaba Dolat,Naghibi, Seyed Amir,Hashemi, Hossein,Ahmadi, Kourosh,Kalantar, Bahareh,&Pradhan, Biswajeet.(2019).Groundwater potential mapping using a novel data-mining ensemble model.HYDROGEOLOGY JOURNAL,27(1),211-224.
MLA	Kordestani, Mojtaba Dolat,et al."Groundwater potential mapping using a novel data-mining ensemble model".HYDROGEOLOGY JOURNAL 27.1(2019):211-224.