Arid
DOI10.1007/s10661-018-6507-8
Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS
Golkarian, Ali1; Naghibi, Seyed Amir2; Kalantar, Bahareh3; Pradhan, Biswajeet4,5
通讯作者Golkarian, Ali
来源期刊ENVIRONMENTAL MONITORING AND ASSESSMENT
ISSN0167-6369
EISSN1573-2959
出版年2018
卷号190期号:3
英文摘要

Ever increasing demand for water resources for different purposes makes it essential to have better understanding and knowledge aboutwater resources. As known, groundwater resources are one of the main water resources especially in countries with arid climatic condition. Thus, this study seeks to provide groundwater potential maps (GPMs) employing new algorithms. Accordingly, this study aims to validate the performance of C5.0, random forest (RF), and multivariate adaptive regression splines (MARS) algorithms for generating GPMs in the eastern part of Mashhad Plain, Iran. For this purpose, a dataset was produced consisting of spring locations as indicator and groundwater-conditioning factors (GCFs) as input. In this research, 13 GCFs were selected including altitude, slope aspect, slope angle, plan curvature, profile curvature, topographic wetness index (TWI), slope length, distance from rivers and faults, rivers and faults density, land use, and lithology. The mentioned dataset was divided into two classes of training and validation with 70 and 30% of the springs, respectively. Then, C5.0, RF, and MARS algorithms were employed using R statistical software, and the final values were transformed into GPMs. Finally, two evaluation criteria including Kappa and area under receiver operating characteristics curve (AUC-ROC) were calculated. According to the findings of this research, MARS had the best performance with AUC-ROC of 84.2%, followed by RF and C5.0 algorithms with AUC-ROC values of 79.7 and 77.3%, respectively. The results indicated that AUC-ROC values for the employed models are more than 70% which shows their acceptable performance. As a conclusion, the produced methodology could be used in other geographical areas. GPMs could be used by water resource managers and related organizations to accelerate and facilitate water resource exploitation.


英文关键词Iran Modeling Mapping R statistical software Geographic information system
类型Article
语种英语
国家Iran ; Malaysia ; Australia ; South Korea
收录类别SCI-E
WOS记录号WOS:000426607700036
WOS关键词ARTIFICIAL NEURAL-NETWORKS ; INFERENCE SYSTEM ANFIS ; DATA MINING MODELS ; FREQUENCY RATIO ; LANDSLIDE SUSCEPTIBILITY ; CLASSIFICATION ; MACHINE ; TREE ; IDENTIFICATION ; PERFORMANCE
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/2XILL650/209057
作者单位1.Ferdowsi Univ Mashhad, Fac Nat Resources & Environm, Mashhad, Iran;
2.Islamic Azad Univ, Mashhad Branch, Young Researchers & Elite Club, Mashhad, Iran;
3.Univ Putra Malaysia, Dept Civil Engn, Fac Engn, Serdang 43400, Selangor, Malaysia;
4.Univ Technol Sydney, Fac Engn & IT, Sch Syst Management & Leadership, POB 123,CB11-06-217,Bldg 11,81 Broadway, Sydney, NSW 2007, Australia;
5.Sejong Univ, Dept Energy & Mineral Resources Engn, 209 Neungdong Ro, Seoul 05006, South Korea
推荐引用方式
GB/T 7714
Golkarian, Ali,Naghibi, Seyed Amir,Kalantar, Bahareh,et al. Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS[J],2018,190(3).
APA Golkarian, Ali,Naghibi, Seyed Amir,Kalantar, Bahareh,&Pradhan, Biswajeet.(2018).Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS.ENVIRONMENTAL MONITORING AND ASSESSMENT,190(3).
MLA Golkarian, Ali,et al."Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS".ENVIRONMENTAL MONITORING AND ASSESSMENT 190.3(2018).
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