干旱区生态环境知识资源中心(Arid): Novel Ensemble Machine Learning Modeling Approach for Groundwater Potential Mapping in Parbhani District of Maharashtra, India

Arid

DOI	10.3390/w15030419
	Novel Ensemble Machine Learning Modeling Approach for Groundwater Potential Mapping in Parbhani District of Maharashtra, India
	Masroor, Md; Sajjad, Haroon; Kumar, Pankaj; Saha, Tamal Kanti; Rahaman, Md Hibjur; Choudhari, Pandurang; Kulimushi, Luc Cimusa; Pal, Swades; Saito, Osamu
通讯作者	Kumar, P
来源期刊	WATER
EISSN	2073-4441
出版年	2023
卷号	15 期号:3
英文摘要	Groundwater is an essential source of water especially in arid and semi-arid regions of the world. The demand for water due to exponential increase in population has created stresses on available groundwater resources. Further, climate change has affected the quantity of water globally. Many parts of Indian cities are experiencing water scarcity. Thus, assessment of groundwater potential is necessary for sustainable utilization and management of water resources. We utilized a novel ensemble approach using artificial neural network multi-layer perceptron (ANN-MLP), random forest (RF), M5 prime (M5P) and support vector machine for regression (SMOReg) models for assessing groundwater potential in the Parbhani district of Maharashtra in India. Ten site-specific influencing factors, elevation, slope, aspect, drainage density, rainfall, water table depth, lineament density, land use land cover, geomorphology, and soil types, were integrated for preparation of groundwater potential zones. The results revealed that the largest area of the district was found under moderate category GWP zone followed by poor, good, very good and very poor. Spatial distribution of GWP zones showed that Poor GWPZs are spread over north, central and southern parts of the district. Very poor GWPZs are mostly found in the north-western and southern parts of the district. The study calls for policy implications to conserve and manage groundwater in these parts. The ensembled model has proved to be effective for assessment of GWP zones. The outcome of the study may help stakeholders efficiently utilize groundwater and devise suitable strategies for its management. Other geographical regions may find the methodology adopted in this study effective for groundwater potential assessment.
英文关键词	groundwater potential zones multi-layer perceptron random forest support vector regression ensemble model Parbhani
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000932572700001
WOS关键词	LOGISTIC-REGRESSION ; VULNERABILITY ; STATE ; AREA
WOS类目	Environmental Sciences ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/398984
推荐引用方式 GB/T 7714	Masroor, Md,Sajjad, Haroon,Kumar, Pankaj,et al. Novel Ensemble Machine Learning Modeling Approach for Groundwater Potential Mapping in Parbhani District of Maharashtra, India[J],2023,15(3).
APA	Masroor, Md.,Sajjad, Haroon.,Kumar, Pankaj.,Saha, Tamal Kanti.,Rahaman, Md Hibjur.,...&Saito, Osamu.(2023).Novel Ensemble Machine Learning Modeling Approach for Groundwater Potential Mapping in Parbhani District of Maharashtra, India.WATER,15(3).
MLA	Masroor, Md,et al."Novel Ensemble Machine Learning Modeling Approach for Groundwater Potential Mapping in Parbhani District of Maharashtra, India".WATER 15.3(2023).