Ensemble modelling framework for groundwater level prediction in urban areas of India

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dc.contributor.author Yadav, Basant
dc.contributor.author Gupta, Pankaj Kumar
dc.contributor.author Patidar, Nitesh
dc.contributor.author Himanshu, Sushil Kumar
dc.date.accessioned 2020-03-16T16:13:25Z
dc.date.available 2020-03-16T16:13:25Z
dc.date.issued 2019-11-24
dc.identifier.citation Yadav B, Gupta PK, Patidar N, Himanshu SK. (2020) Ensemble modelling framework for groundwater level prediction in urban areas of India. Science of the Total Environment, Volume 712, April 2020, Article number 135539 en_UK
dc.identifier.issn 0048-9697
dc.identifier.uri https://doi.org/10.1016/j.scitotenv.2019.135539
dc.identifier.uri http://dspace.lib.cranfield.ac.uk/handle/1826/15288
dc.description.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. en_UK
dc.language.iso en en_UK
dc.publisher Elsevier en_UK
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/ *
dc.subject Machine learning en_UK
dc.subject Mutual information en_UK
dc.subject Genetic algorithm en_UK
dc.subject Artificial neural network en_UK
dc.subject Support vector machine en_UK
dc.subject Urbanization en_UK
dc.title Ensemble modelling framework for groundwater level prediction in urban areas of India en_UK
dc.type Article en_UK

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