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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/27041
Title: Ensemble modelling framework for groundwater level prediction in urban areas of India
Authors: Yadav, Basant
Gupta P.K.
Patidar N.
Himanshu S.K.
Published in: Science of the Total Environment
Abstract: India is facing the worst water crisis in its history and major Indian cities which accommodate about 50% of its population will be among highly groundwater stressed cities by 2020. In past few decades, the urban groundwater resources declined significantly due to over exploitation, urbanization, population growth and climate change. To understand the role of these variables on groundwater level fluctuation, we developed a machine learning based modelling approach considering singular spectrum analysis (SSA), mutual information theory (MI), genetic algorithm (GA), artificial neural network (ANN) and support vector machine (SVM). The developed approach was used to predict the groundwater levels in Bengaluru, a densely populated city with declining groundwater water resources. The input data which consist of groundwater levels, rainfall, temperature, NOI, SOI, NIÑO3 and monthly population growth rate were pre-processed using mutual information theory, genetic algorithm and lag analysis. Later, the optimized input sets were used in ANN and SVM to predict monthly groundwater level fluctuations. The results suggest that the machine learning based approach with data pre-processing predict groundwater levels accurately (R > 85%). It is also evident from the results that the pre-processing techniques enhance the prediction accuracy and results were improved for 66% of the monitored wells. Analysis of various input parameters suggest, inclusion of population growth rate is positively correlated with decrease in groundwater levels. The developed approach in this study for urban groundwater prediction can be useful particularly in cities where lack of pipeline/sewage/drainage lines leakage data hinders physical based modelling. © 2019 Elsevier B.V.
Citation: Science of the Total Environment, 712
URI: https://doi.org/10.1016/j.scitotenv.2019.135539
http://repository.iitr.ac.in/handle/123456789/27041
Issue Date: 2020
Publisher: Elsevier B.V.
Keywords: Artificial neural network
Genetic algorithm
Machine learning
Mutual information
Support vector machine
Urbanization
ISSN: 489697
Author Scopus IDs: 56519355800
57214156754
56321625400
57190672874
Author Affiliations: Yadav, B., Cranfield Water Science Institute, Cranfield University, Vincent Building, Cranfield, Bedford, MK43 0AL, Ireland
Gupta, P.K., Faculty of Environment, University of Waterloo, 200 University Ave W, Waterloo, ON N2L 3G1, Canada
Patidar, N., Groundwater Hydrology Division, National Institute of Hydrology, Roorkee, Uttarakhand 247667, India
Himanshu, S.K., Texas A&M Agrilife Research, Texas A&M University System, Vernon, TX, United States
Funding Details: This study was supported by National Postdoctoral Fellowship (NPDF) grant ( PDF/2017/000415 ) funded by Science and Engineering Research Board (SERB), India. The authors would like to acknowledge the District Groundwater Office, Groundwater Directorate Bengaluru, Karnataka for supplying the data. PDF/2017/000415; Science and Engineering Research Board, SERB
Corresponding Author: Yadav, B.; Cranfield Water Science Institute, Vincent Building, Cranfield, Ireland; email: Basant.Yadav@cranfield.ac.uk
Appears in Collections:Journal Publications [WR]

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