Abstract:
India is facing the worst water crisis in its history and major Indian cities which accommodate about 50% of its
populationwill be among highly groundwater stressed cities by 2020. In past fewdecades, 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
algorithmand lag analysis. Later, the optimized input setswere used inANNand SVMto predictmonthly groundwater
level fluctuations. The results suggest that the machine learning based approach with data pre-processing
predict groundwater levels accurately (R N 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.
