Keywords: support vector machines, SVM, groundwater levels, forecasting, India, autoregressive moving average model, ARMA, artificial neural networks, ANN, adaptive neuro fuzzy inference system, ANFIS, particle swarm optimisation, PSO, fuzzy logic, groundwater management, water management, groundwater resources, water levels
Groundwater level forecasting using SVM–PSO
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.