干旱区生态环境知识资源中心(Arid): Spatial modelling for identification of groundwater potential zones in semi-arid ecosystem of southern India using Sentinel-2 data, GIS and bivariate statistical models

Arid

DOI	10.1007/s12517-021-07669-0
	Spatial modelling for identification of groundwater potential zones in semi-arid ecosystem of southern India using Sentinel-2 data, GIS and bivariate statistical models
	Kumar, Karikkathil C. Arun; Reddy, Gangalakunta P. Obi; Masilamani, Palanisamy; Sandeep, Pundoor
通讯作者	Reddy, GPO (corresponding author), ICAR Natl Bur Soil Survey & Land Use Planning, Div Remote Sensing Applicat, Amravati Rd, Nagpur 440033, Maharashtra, India.
来源期刊	ARABIAN JOURNAL OF GEOSCIENCES
ISSN	1866-7511
EISSN	1866-7538
出版年	2021
卷号	14 期号:14
英文摘要	The overarching goal of the present investigation is to adopt GIS-based spatial modelling techniques to delineate the groundwater potential zones (GWPZs) in Sarabanga watershed (SBW) of Salem district, Tamil Nadu (TN) state of southern India, by using high-resolution Sentinel-2 data, geographic information system (GIS), and bivariate statistical models (BSM) of frequency ratio (FR), and index of entropy (IoE). In GIS-based spatial modelling, eight contributing factors to groundwater potential (GWP), which includes geology, geomorphology, drainage density (Dd), slope, lineament density (Ld), soil texture, rainfall, land use/land cover (LU/LC), and the well inventory data of 135 well locations were considered in identification of GWPZs. The identified GWPZs of SBW based on the FR, and IoE models show that about 67.8% and 66.1% area of SBW are under very good to excellent categories, while 9.0% and 8.1% are under poor, and very poor categories. The results obtained were validated by using 'Area Under the Curve-Receiver Operating Characteristic' (AUC-ROC) method with the validation data and observed the prediction rate of 0.7313 and 0.7084, for FR, and IoE models, respectively. Modelling of GWPZs shows that FR model clearly exhibits its robustness over the IoE model. Sensitivity analysis performed through Variable Importance Analysis (VIA) indicates that in both FR, and IoE models, geology, slope, rainfall, and Dd were identified as the most influencing factors in delineation of GWPZs. The study clearly demonstrates the potential of Sentinal-2A data, GIS-based spatial modelling, and robustness of FR, and IoE models in attaining the reliable, and cost-effective results in delineation of GWPZs, which helps immensely in development of GW exploration, and management plans.
英文关键词	Sentinel-2 data Groundwater Spatial modelling Semi-arid ecosystem Bivariate statistical models
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000691615900001
WOS关键词	HARD-ROCK TERRAIN ; FREQUENCY RATIO ; RIVER-BASIN ; LOGISTIC-REGRESSION ; INFORMATION-SYSTEM ; HIERARCHY PROCESS ; ENTROPY MODELS ; TAMIL-NADU ; DISTRICT ; RECHARGE
WOS类目	Geosciences, Multidisciplinary
WOS研究方向	Geology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/368628
作者单位	[Kumar, Karikkathil C. Arun; Reddy, Gangalakunta P. Obi; Sandeep, Pundoor] ICAR Natl Bur Soil Survey & Land Use Planning, Div Remote Sensing Applicat, Amravati Rd, Nagpur 440033, Maharashtra, India; [Kumar, Karikkathil C. Arun; Masilamani, Palanisamy; Sandeep, Pundoor] Bharathidasan Univ, Dept Geog, Tiruchirappalli 620024, Tamil Nadu, India
推荐引用方式 GB/T 7714	Kumar, Karikkathil C. Arun,Reddy, Gangalakunta P. Obi,Masilamani, Palanisamy,et al. Spatial modelling for identification of groundwater potential zones in semi-arid ecosystem of southern India using Sentinel-2 data, GIS and bivariate statistical models[J],2021,14(14).
APA	Kumar, Karikkathil C. Arun,Reddy, Gangalakunta P. Obi,Masilamani, Palanisamy,&Sandeep, Pundoor.(2021).Spatial modelling for identification of groundwater potential zones in semi-arid ecosystem of southern India using Sentinel-2 data, GIS and bivariate statistical models.ARABIAN JOURNAL OF GEOSCIENCES,14(14).
MLA	Kumar, Karikkathil C. Arun,et al."Spatial modelling for identification of groundwater potential zones in semi-arid ecosystem of southern India using Sentinel-2 data, GIS and bivariate statistical models".ARABIAN JOURNAL OF GEOSCIENCES 14.14(2021).