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- W2124366144 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." @default.
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- W2124366144 date "2012-01-01" @default.
- W2124366144 modified "2023-10-16" @default.
- W2124366144 title "Groundwater level forecasting using SVM-PSO" @default.
- W2124366144 doi "https://doi.org/10.1504/ijhst.2012.047432" @default.
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