Groundwater level forecasting using SVM-QPSO

Authors:
Ch. Sudheer;Nitin Anand Shrivastava;Bijaya Ketan Panigrahi;Shashi Mathur
Affiliations:
Department of Civil Engineering, Indian Institute of Technology, New Delhi, India;Department of Electrical Engineering, Indian Institute of Technology, New Delhi, India;Department of Electrical Engineering, Indian Institute of Technology, New Delhi, India;Department of Civil Engineering, Indian Institute of Technology, New Delhi, India
Venue:
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Year:
2011

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Abstract

Forecasting the groundwater levels in a water basin plays a significant role in the the management of groundwater resources. In this study, Support Vector Machines (SVM) is used to construct a ground water level forecasting system. Further Quantum behaved Particle Swarm Optimization function is adapted in this study to determine the SVM parameters. Later, the proposed SVM-QPSO model is employed in estimating the groundwater level of Rentachintala region of Andhra Pradesh in India. The performance of the SVM-QPSO model is then compared with the ANN (Artificial Neural Networks). The results indicate that SVM-QPSO is a far better technique for predicting groundwater levels as it provides a high degree of accuracy and reliability.