干旱区生态环境知识资源中心(Arid): Application of Probabilistic and Machine Learning Models for Groundwater Potentiality Mapping in Damghan Sedimentary Plain, Iran

Arid

DOI	10.3390/rs11243015
	Application of Probabilistic and Machine Learning Models for Groundwater Potentiality Mapping in Damghan Sedimentary Plain, Iran
	Arabameri, Alireza 1; Roy, Jagabandhu 2; Saha, Sunil 2; Blaschke, Thomas 3; Ghorbanzadeh, Omid 3; Dieu Tien Bui 4
通讯作者	Dieu Tien Bui
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2019
卷号	11 期号:24
英文摘要	Groundwater is one of the most important natural resources, as it regulates the earth's hydrological system. The Damghan sedimentary plain area, located in the region of a semi-arid climate of Iran, has very critical conditions of groundwater due to massive pressure on it and is in need of robust models for identifying the groundwater potential zones (GWPZ). The main goal of the current research is to prepare a groundwater potentiality map (GWPM) considering the probabilistic, machine learning, data mining, and multi-criteria decision analysis (MCDA) approaches. For this purpose, 80 wells collected from the Iranian groundwater resource department and field investigation with global positioning system (GPS), have been selected randomly and considered as the groundwater inventory datasets. Out of 80 wells, 56 (70%) wells have been brought into play for modeling and 24 (30%) for validation purposes. Elevation, slope, aspect, convergence index (CI), rainfall, drainage density (Dd), distance to river, distance to fault, distance to road, lithology, soil type, land use/land cover (LU/LC), normalized difference vegetation index (NDVI), topographic wetness index (TWI), topographic position index (TPI), and stream power index (SPI) have been used for modeling purpose. The area under the receiver operating characteristic (AUROC), sensitivity (SE), specificity (SP), accuracy (AC), mean absolute error (MAE), and root mean square error (RMSE) are used for checking the goodness-of-fit and prediction accuracy of approaches to compare their performance. In addition, the influence of groundwater determining factors (GWDFs) on groundwater occurrence was evaluated by performing a sensitivity analysis model. The GWPMs, produced by technique for order preference by similarity to ideal solution (TOPSIS), random forest (RF), binary logistic regression (BLR), weight of evidence (WoE) and support vector machine (SVM) have been classified into four categories, i.e., low, medium, high and very high groundwater potentiality with the help of the natural break classification methods in the GIS environment. The very high groundwater potentiality class is covered 15.09% for TOPSIS, 15.46% for WoE, 25.26% for RF, 15.47% for BLR, and 18.74% for SVM of the entire plain area. Based on sensitivity analysis, distance from river, and drainage density represent significantly effects on the groundwater occurrence. validation results show that the BLR model with best prediction accuracy and goodness-of-fit outperforms the other five models. Although, all models have very good performance in modeling of groundwater potential. Results of seed cell area index model that used for checking accuracy classification of models show that all models have suitable performance. Therefore, these are promising models that can be applied for the GWPZs identification, which will help for some needful action of these areas.
英文关键词	groundwater potential mapping (GWPM) probabilistic models machine learning algorithms sensitivity analysis Damghan sedimentary plain
类型	Article
语种	英语
国家	Iran ; India ; Austria ; Vietnam
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000507333400129
WOS关键词	WEIGHTS-OF-EVIDENCE ; SUPPORT VECTOR MACHINE ; RANDOM FOREST ; SENSITIVITY-ANALYSIS ; LOGISTIC-REGRESSION ; SPATIAL PREDICTION ; FREQUENCY RATIO ; DECISION TREE ; SUSCEPTIBILITY ; MULTIVARIATE
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
EI主题词	2019-12-02
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/311499
作者单位	1.Tarbiat Modares Univ, Dept Geomorphol, Tehran 1411713116, Iran; 2.Univ Gour Banga, Dept Geog, Malda 732103, W Bengal, India; 3.Univ Salzburg, Dept Geoinformat Z GIS, A-5020 Salzburg, Austria; 4.Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam
推荐引用方式 GB/T 7714	Arabameri, Alireza,Roy, Jagabandhu,Saha, Sunil,et al. Application of Probabilistic and Machine Learning Models for Groundwater Potentiality Mapping in Damghan Sedimentary Plain, Iran[J],2019,11(24).
APA	Arabameri, Alireza,Roy, Jagabandhu,Saha, Sunil,Blaschke, Thomas,Ghorbanzadeh, Omid,&Dieu Tien Bui.(2019).Application of Probabilistic and Machine Learning Models for Groundwater Potentiality Mapping in Damghan Sedimentary Plain, Iran.REMOTE SENSING,11(24).
MLA	Arabameri, Alireza,et al."Application of Probabilistic and Machine Learning Models for Groundwater Potentiality Mapping in Damghan Sedimentary Plain, Iran".REMOTE SENSING 11.24(2019).