干旱区生态环境知识资源中心(Arid): GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches

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DOI	10.1016/j.scitotenv.2018.12.115
	GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches
	Arabameri, Alireza 1; Rezaei, Khalil 2; Cerda, Artemi 3; Lombardo, Luigi 4; Rodrigo-Comino, Jesus 5
通讯作者	Arabameri, Alireza
来源期刊	SCIENCE OF THE TOTAL ENVIRONMENT
ISSN	0048-9697
EISSN	1879-1026
出版年	2019
卷号	658 页码:160-177
英文摘要	In and and semi-arid areas, groundwater resource is one of the most important water sources by the humankind. Knowledge of groundwater distribution over space, associated flow and basic exploitation measures can play a significant role in planning sustainable development, especially in and and semi-arid areas. Groundwater potential mapping (GWPM) fits in this context as the tool used to predict the spatial distribution of groundwater. In this research we tested four GIS-based models for GWPM, consisting of: i) random forest (RF); ii) weight of evidence (WoE); binary logistic regression (BLR); and iv) technique for order preference by similarity to ideal solution (TOPSIS) multi-criteria. The Shahroud plain located in Iran, was selected to research the water scarcity and over exploitation of groundwater resources over the past 20 years. In this research, using Iranian Department of Water Resources Management data, and extensive field surveys, 122 groundwater well data with high potential yield of >= 11 m(3) h(-1) were selected for GWPM. Specifically, we generated four different models selecting 70% (n = 85) of the wells and validated the resulting GWP maps upon the complementary 30% (n = 37). A total of fifteen ground water conditioning factors to explain the groundwater well distribution over the Shahroud plain were selected. From the Advanced Land Observing Satellite (ALOS), a DEM (30 m resolution) was extracted to calculate a set of morphometric properties which were combined with thematic ones such as land use,land cover (LU/LC) and Soil Type (ST). Results show that in RF (LU/LC), LR (ST), and AHP (Slope) are the most relevant contributors to groundwater occurrence. After that, using the natural break method, final maps were divided into five susceptibility classes of very low, low, moderate, high, and very high. The accuracy of models was ultimately tested using prediction rate (validation data), success rate (training data) and the seed cell area index (SCAI) indicators. Results of validation show that BLR with prediction rate of 0.905 (90.5%) and success rate of 0.918 (91.8%) had higher accuracy than WoE, RF and TOPSIS models with respective prediction rates of 0.885, 0.873 and 0.870 (88.5%, 873%, and 87%) and success rate of 0.900, 0.889, and 0.881 (90%, 88.9%, and 88.1%). SCAT results show that all models have acceptable classification accuracy although BLR outperformed the other models in terms of accuracy. Results show that the combination of remote sensing (RS) data and geographic information system (GIS) with new approaches can be used as a powerful tool in GWPM in arid and semi-arid areas. The results of this investigation introduced a potential novel methodology that could be used by decision-makers for the sustainable management of ground water resources. (C) 2018 Elsevier B.V. All rights reserved.
英文关键词	Random forest Weight of evidence Binary logistic regression Decision making Semi-arid region
类型	Article
语种	英语
国家	Iran ; Spain ; Netherlands
收录类别	SCI-E
WOS记录号	WOS:000456175700019
WOS关键词	WEIGHTS-OF-EVIDENCE ; BINARY LOGISTIC-REGRESSION ; MACHINE LEARNING-MODELS ; BELIEF FUNCTION MODEL ; LANDSLIDE SUSCEPTIBILITY ; RANDOM-FOREST ; SPATIAL PREDICTION ; FREQUENCY RATIO ; TAMIL-NADU ; RECHARGE ZONES
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/2XILL650/218652
作者单位	1.Tarbiat Modares Univ, Dept Geomorphol, Tehran 3658117994, Iran; 2.Kharazmi Univ, Fac Earth Sci, Tehran 1491115719, Iran; 3.Univ Valencia, Dept Geog, Soil Eros & Degradat Res Grp, Blasco Ibanez 28, Valencia 46010, Spain; 4.Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, Enschede, Netherlands; 5.Univ Malaga, Inst Geomorfol & Suelos, Edificio Ada Byron,Ampliac Campus Teatinos, E-29071 Malaga, Spain
推荐引用方式 GB/T 7714	Arabameri, Alireza,Rezaei, Khalil,Cerda, Artemi,et al. GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches[J],2019,658:160-177.
APA	Arabameri, Alireza,Rezaei, Khalil,Cerda, Artemi,Lombardo, Luigi,&Rodrigo-Comino, Jesus.(2019).GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches.SCIENCE OF THE TOTAL ENVIRONMENT,658,160-177.
MLA	Arabameri, Alireza,et al."GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches".SCIENCE OF THE TOTAL ENVIRONMENT 658(2019):160-177.