Groundwater level forecasting using SVM-PSO Online publication date: Sat, 16-Aug-2014
by Sudheer Ch; Shashi Mathur
International Journal of Hydrology Science and Technology (IJHST), Vol. 2, No. 2, 2012
Abstract: The prediction of groundwater levels in a basin is of immense importance for the management of groundwater resources. In this study, support vector machines (SVMs) is used to construct a ground water level forecasting system. Further the proposed SVM-PSO model is employed in estimating the groundwater level of Rentachintala region of Andhra Pradesh in India. The SVM-PSO model with various input structures is constructed and the best structure is determined using the k-fold cross validation method. Further particle swarm optimisation function is adapted in this study to determine the optimal values of SVM parameters. Later, the performance of the SVM-PSO model is compared with the autoregressive moving average model (ARMA), artificial neural networks (ANN) and adaptive neuro fuzzy inference system (ANFIS). The results indicate that SVM-PSO is a far better technique for predicting groundwater levels as it provides a high degree of accuracy and reliability.
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