干旱区生态环境知识资源中心(Arid): Integration of statistical models and ensemble machine learning algorithms (MLAs) for developing the novel hybrid groundwater potentiality models: a case study of semi-arid watershed in Saudi Arabia

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DOI	10.1080/10106049.2021.1939439
	Integration of statistical models and ensemble machine learning algorithms (MLAs) for developing the novel hybrid groundwater potentiality models: a case study of semi-arid watershed in Saudi Arabia
	Mallick, Javed; Talukdar, Swapan; Alsubih, Majed; Almesfer, Mohammed K.; Shahfahad; Hang, Hoang Thi; Rahman, Atiqur
通讯作者	Mallick, J (corresponding author), King Khalid Univ, Coll Engn, Dept Civil Engn, Abha, Saudi Arabia.
来源期刊	GEOCARTO INTERNATIONAL
ISSN	1010-6049
EISSN	1752-0762
出版年	2021-06
英文摘要	The present study has proposed three novel hybrid models by integrating three traditional ensemble models, such as random forest, logitboost, and naive bayes, and six newly developed ensemble models of rotation forest (RF), such as decision tree (RF-DT), J48 (DF-J48), naive bayes tree (RF-NBT), neural network (RF-NN), M5P (RF-M5P) and REPTree (RF-REPTree), with three statistical models, i.e. weight of evidence, logistic regression and combination of WOE and LR. To predict the groundwater potential, nine groundwater potential conditioning parameters have been created. The Information Gain Ratio has been used to evaluate the impact of each parameter. The ROC curve has been used to validate the models. According to the findings, 15 to 30% of the study area has a very high or high groundwater potentiality. Furthermore, validation results revealed that RF based ensembles models outperformed other standalone models for groundwater potential modelling.
英文关键词	Groundwater potential modelling ensemble machine learning algorithm statistical models novel hybrid models information gain ratio
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000667573600001
WOS关键词	WEIGHTS-OF-EVIDENCE ; ARTIFICIAL NEURAL-NETWORKS ; EVIDENTIAL BELIEF FUNCTION ; NAIVE BAYES TREE ; LOGISTIC-REGRESSION ; LANDSLIDE SUSCEPTIBILITY ; FLOOD SUSCEPTIBILITY ; ROTATION FOREST ; DECISION-TREE ; SPATIAL PREDICTION
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/2XILL650/367534
作者单位	[Mallick, Javed; Alsubih, Majed] King Khalid Univ, Coll Engn, Dept Civil Engn, Abha, Saudi Arabia; [Talukdar, Swapan] Univ Gour Banga, Dept Geog, Malda, India; [Almesfer, Mohammed K.; Hang, Hoang Thi] King Khalid Univ, Coll Engn, Dept Chem Engn, Abha, Saudi Arabia; [Shahfahad; Rahman, Atiqur] Jamia Millia Islamia, Fac Nat Sci, Dept Geog, New Delhi, India
推荐引用方式 GB/T 7714	Mallick, Javed,Talukdar, Swapan,Alsubih, Majed,et al. Integration of statistical models and ensemble machine learning algorithms (MLAs) for developing the novel hybrid groundwater potentiality models: a case study of semi-arid watershed in Saudi Arabia[J],2021.
APA	Mallick, Javed.,Talukdar, Swapan.,Alsubih, Majed.,Almesfer, Mohammed K..,Shahfahad.,...&Rahman, Atiqur.(2021).Integration of statistical models and ensemble machine learning algorithms (MLAs) for developing the novel hybrid groundwater potentiality models: a case study of semi-arid watershed in Saudi Arabia.GEOCARTO INTERNATIONAL.
MLA	Mallick, Javed,et al."Integration of statistical models and ensemble machine learning algorithms (MLAs) for developing the novel hybrid groundwater potentiality models: a case study of semi-arid watershed in Saudi Arabia".GEOCARTO INTERNATIONAL (2021).