干旱区生态环境知识资源中心(Arid): Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential

Arid

DOI	10.1080/10106049.2021.1920635
	Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential
	Chen, Yunzhi; Chen, Wei; Pal, Subodh Chandra; Saha, Asish; Chowdhuri, Indrajit; Adeli, Behzad; Janizadeh, Saeid; Dineva, Adrienn A.; Wang, Xiaojing; Mosavi, Amirhosein
通讯作者	Mosavi, A (corresponding author), Obuda Univ, John von Neumann Fac Informat, Budapest, Hungary. ; Mosavi, A (corresponding author), Ton Duc Thang Univ, Environm Qual Atmospher Sci & Climate Change Res, Ho Chi Minh City, Vietnam. ; Mosavi, A (corresponding author), Ton Duc Thang Univ, Fac Environm & Labour Safety, Ho Chi Minh City, Vietnam.
来源期刊	GEOCARTO INTERNATIONAL
ISSN	1010-6049
EISSN	1752-0762
出版年	2021-04
英文摘要	Delineation of the groundwater's potential zones is a growing phenomenon worldwide due to the high demand for fresh groundwater. Therefore, the identification of potential groundwater zones is an important tool for groundwater occurrence, protection, and management purposes. More specifically, in arid and semi-arid regions, groundwater is one of the most important natural resources as it supplies water during the drought period. The present research study focused on the delineation of potential groundwater zones in Saveh City, the northern part of the Markazi Province in Iran. The groundwater potential mapping was prepared using hybrid deep learning and machine learning algorithm of the boosted tree (BT), artificial neural network (ANN), deep learning neural network (DLNN), deep learning tree (DLT), and deep boosting (DB). This study was carried out by using fourteen groundwater potential conditioning factors and 349 each for springs and non-springs points. The performance of each model was validated through statistical analysis of sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and receiver operating characteristic (ROC)-area under curve (AUC) analysis. The validation result showed that the success rate of AUC is very good for the DB model (0.87-0.99) and other models are also good i.e. BT (0.81-0.90), ANN (0.77-0.82), DLNN (0.84-0.86), and DLT (0.83-0.91). Among the several factors used in this study altitude, rainfall, distance to fault and soil types are the more important conditioning factors for groundwater potential modeling. Finally, all the models in this study had high efficiency in groundwater potential mapping, but it is recommended to use the Deep Boost model due to the better results in future studies. The result of this work will be useful to planners for optimal use and future planning of groundwater.
英文关键词	Groundwater potential mapping groundwater management hybrid deep learning deep boosting ROC-AUC artificial intelligence
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000651278800001
WOS关键词	FREQUENCY RATIO MODEL ; DATA MINING MODELS ; SPATIAL PREDICTION ; SEMIARID REGION ; RANDOM-FOREST ; RIVER-BASIN ; WEST-BENGAL ; GIS ; MACHINE ; ENSEMBLE
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
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/352204
作者单位	[Chen, Yunzhi; Chen, Wei; Wang, Xiaojing] Xian Univ Sci & Technol, Coll Geol & Environm, Xian, Peoples R China; [Chen, Wei] Minist Nat Resources, Key Lab Coal Resources Explorat & Comprehens Util, Xian, Peoples R China; [Pal, Subodh Chandra; Saha, Asish; Chowdhuri, Indrajit] Univ Burdwan, Dept Geog, Bardhaman, W Bengal, India; [Adeli, Behzad] Petro Omid Asia POA, Watershed Management Engn Dept, Tehran, Iran; [Janizadeh, Saeid] Tarbiat Modares Univ, Fac Nat Resources & Marine Sci, Dept Watershed Management Engn & Sci, Tehran, Iran; [Dineva, Adrienn A.; Mosavi, Amirhosein] Obuda Univ, John von Neumann Fac Informat, Budapest, Hungary; [Mosavi, Amirhosein] Ton Duc Thang Univ, Environm Qual Atmospher Sci & Climate Change Res, Ho Chi Minh City, Vietnam; [Mosavi, Amirhosein] Ton Duc Thang Univ, Fac Environm & Labour Safety, Ho Chi Minh City, Vietnam
推荐引用方式 GB/T 7714	Chen, Yunzhi,Chen, Wei,Pal, Subodh Chandra,et al. Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential[J],2021.
APA	Chen, Yunzhi.,Chen, Wei.,Pal, Subodh Chandra.,Saha, Asish.,Chowdhuri, Indrajit.,...&Mosavi, Amirhosein.(2021).Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential.GEOCARTO INTERNATIONAL.
MLA	Chen, Yunzhi,et al."Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential".GEOCARTO INTERNATIONAL (2021).