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.
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Sudheer, C., Shrivastava, N.A., Panigrahi, B.K., Mathur, S. (2011). Groundwater Level Forecasting Using SVM-QPSO. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27172-4_86
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DOI: https://doi.org/10.1007/978-3-642-27172-4_86
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