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Groundwater Level Prediction using M5 Model Trees

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Abstract

Groundwater is an important resource, readily available and having high economic value and social benefit. Recently, it had been considered a dependable source of uncontaminated water. During the past two decades, increased rate of extraction and other greedy human actions have resulted in the groundwater crisis, both qualitatively and quantitatively. Under prevailing circumstances, the availability of predicted groundwater levels increase the importance of this valuable resource, as an aid in the planning of groundwater resources. For this purpose, data-driven prediction models are widely used in the present day world. M5 model tree (MT) is a popular soft computing method emerging as a promising method for numeric prediction, producing understandable models. The present study discusses the groundwater level predictions using MT employing only the historical groundwater levels from a groundwater monitoring well. The results showed that MT can be successively used for forecasting groundwater levels.

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References

  1. P.C. Nayak, Y.R. Satyaji Rao, K.P. Sudheer, Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water Resour. Manag 20, 77–90 (2006)

    Article  Google Scholar 

  2. L.-H. Cheng, C.-T. Chen, D.-W. Lin, Application of integrated back propagation network and self organizing map for ground water level forecasting. J. Water Resour. Planning Manag 137(4), 352–365 (2011)

    Article  Google Scholar 

  3. Z. Gadampour, G. Rakshendehroo, Using artificial neural network to forecast groundwater depth in union county well, in Proceedings of World Academy of Science Engineering and Technology, 2010

  4. M. Kavitha Mayilvaganan, K.B. Naidu, Application of soft computing techniques for groundwater level forecasting, in Proceedings of 2012 International Conference on Computer Networks and Communication Systems, vol 35, Singapore, 2012

  5. D.P. Solomatine, Data–driven modeling: paradigm, methods, experiences, in Proceedings of 5th International Conference on Hydroinformatics, Cardiff, 2002

  6. D.P. Solomatine, M.B.L.A. Siek, Flexible and Optimal M5 Model Trees with applications to flow predictions, in 6th International Conference on Hydroinformatics (2004)

  7. D.P. Solomatine, K.N. Dulal, Model tree as an alternative to neural network in prediction rainfall-runoff modeling. Hydrol Sci. J. 48(3), 399–411 (2003)

    Article  Google Scholar 

  8. Y. Wang, I. Witten, Induction of model trees for predicting continuous classes, in Proceedings of the poster papers of the European Conference on Machine Learning, Prague, 1997, pp. 128–137

  9. J. Ross Quilan, Induction of decision trees. Mach Learn Mach 1, 81–106 (1986)

    Google Scholar 

  10. E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten, Using model trees for classification. Mach Learn 32(1), 63–76 (1998)

    Article  MATH  Google Scholar 

  11. R. Sanderson, Regression Prediction, COMP527: Data Mining, Dept. Computer Science, Liverpool Univ., 2008, http://www.csc.liv.ac.uk

  12. G. Jekabsons, M5PrimeLab: M5’ regression tree and model tree toolbox Matlab/Octave, (2010), available at http://www.cs.rtu.lv/jekabsons/

  13. I.H. Witten, E. Frank, M.A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. (Morgan Kaufmann, Burlington, 2011)

    Google Scholar 

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Correspondence to Nitha Ayinippully Nalarajan.

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Nalarajan, N.A., Mohandas, C. Groundwater Level Prediction using M5 Model Trees. J. Inst. Eng. India Ser. A 96, 57–62 (2015). https://doi.org/10.1007/s40030-014-0093-8

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  • DOI: https://doi.org/10.1007/s40030-014-0093-8

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