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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/24805
Title: An integration of geospatial and machine learning techniques for mapping groundwater potential: a case study of the Shipra river basin, India
Authors: Patidar R.
Pingale S.M.
Khare, Deepak
Published in: Arabian Journal of Geosciences
Abstract: Groundwater is an important component of the hydrologic cycle and its significance is quite high due to the lack of surface water and is an important source of fresh water. The amount of surface water alone is not enough to meet the demands of increasing population and increased needs for different purposes due to technological advances. Hence, the need of the hour is to increase groundwater sources and manage them effectively for their sustainable growth. Therefore, the main objective of this study is to map groundwater potential (GWP) zones of the Shipra river basin in India using advanced machine learning and geospatial techniques. Nine factors were used as effective factors such as slope degree, altitude, plan curvature, topographic wetness index (TWI), profile curvature, topographic factor, drainage density, slope aspect, and land use/land cover. The models adopted in this study were classification and regression tree (CART), boosted regression tree (BRT), and random forest (RF). The integrated results of GIS and machine learning techniques were proved to be effective and successful in predicting GWP zones. The area under the curve (AUC) of three models namely BRT, CART, and RF came out to be 0.841, 0.880, and 0.899, respectively. This indicates that all the models are giving a good performance for the GWP zone mapping (>0.80). This study also found that the best technique for prediction is random forest followed by CART and BRT in the case of Shipra river basin. Therefore, this study outcome can prove to be beneficial for effective management, protection, and exploration of groundwater prospects for the different stakeholders. © 2021, Saudi Society for Geosciences.
Citation: Arabian Journal of Geosciences, 14(16)
URI: https://doi.org/10.1007/s12517-021-07871-0
http://repository.iitr.ac.in/handle/123456789/24805
Issue Date: 2021
Publisher: Springer Science and Business Media Deutschland GmbH
Keywords: Boosted regression tree
Classification and regression tree
GIS
Groundwater potential zones
Machine learning techniques
Random forest
ISSN: 18667511
Author Scopus IDs: 57226565513
55921235700
14060295600
Author Affiliations: Patidar, R., Department of Water Resources Development and Management, Indian Institute of Technology, Roorkee, India
Pingale, S.M., Hydrological Investigations Division, National Institute of Hydrology, Roorkee, India
Khare, D., Department of Water Resources Development and Management, Indian Institute of Technology, Roorkee, India
Funding Details: The authors thankfully acknowledge the Central Groundwater Board (CGWB) for providing the required data. We also acknowledge the USGS Earth Resources Observation portal for providing necessary data. We also acknowledge Indian Institute of Technology Roorkee for providing financial support. We would also like to thank the anonymous reviewers and editors for their critical comments which significantly improved the manuscript. U.S. Geological Survey, USGS; Indian Institute of Technology Roorkee, IITR
Corresponding Author: Pingale, S.M.; Hydrological Investigations Division, India; email: pingalesm.nihr@gov.in
Appears in Collections:Journal Publications [WR]

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