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Groundwater Level Forecasting Using SVM-QPSO

  • Ch. Sudheer
  • Nitin Anand Shrivastava
  • Bijaya Ketan Panigrahi
  • Shashi Mathur
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7076)

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.

Keywords

Support Vector Machine Root Mean Square Error Groundwater Level Support Vector Regression Support Vector Machine Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ch. Sudheer
    • 1
  • Nitin Anand Shrivastava
    • 2
  • Bijaya Ketan Panigrahi
    • 2
  • Shashi Mathur
    • 1
  1. 1.Department of Civil EngineeringIndian Institute of TechnologyNew DelhiIndia
  2. 2.Department of Electrical EngineeringIndian Institute of TechnologyNew DelhiIndia

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