Skip to main content
SpringerLink
Log in

Multistep Ahead Groundwater Level Time-Series Forecasting Using Gaussian Process Regression and ANFIS

  • Chapter
  • First Online:
Advanced Computing and Systems for Security

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 396))

Abstract

Groundwater level is regarded as an environmental indicator to quantify groundwater resources and their exploitation. In general, groundwater systems are characterized by complex and nonlinear features. Gaussian Process Regression (GPR) approach is employed in the present study to investigate its applicability in probabilistic forecasting of monthly groundwater level fluctuations at two shallow unconfined aquifers located in the Kumaradhara river basin near Sullia Taluk, India. A series of monthly groundwater level observations monitored during the period 2000–2013 is utilized for the simulation. Univariate time-series GPR and Adaptive Neuro Fuzzy Inference System (ANFIS) models are simulated and applied for multistep lead time forecasting of groundwater levels. Individual performance of the GPR and ANFIS models are comparatively evaluated using various statistical indices. In overall, simulation results reveal that GPR model provided reasonably accurate predictions than that of ANFIS during both training and testing phases. Thus, an effective GPR model is found to generate more precise probabilistic forecasts of groundwater levels.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Alley, W.M., Reilly, T.E., Franke, O.L.: Sustainability of Ground-Water Resources, p. 79. U.S. Geological Survey Circular 1186, Denver (1999)

    Google Scholar 

  2. Raghavendra, N.S., Deka, P.C.: Sustainable development and management of groundwater resources in mining affected areas. Procedia Earth Planet. Sci. 11, 598–604 (2015). doi:10.1016/j.proeps.2015.06.061

    Article  Google Scholar 

  3. Taylor, C.J., Alley, W.M.: Ground-Water-Level Monitoring and the Importance of Long-Term Water-Level Data, p. 67. U.S. Geological Survey Circular 1217, Denver (2001)

    Google Scholar 

  4. Gupta, A.D., Onta, P.R.: Sustainable groundwater resources development. Hydrol. Sci. J. 42, 565–582 (1997)

    Google Scholar 

  5. Adamowski, K., Hamory, T.: A stochastic systems model of groundwater level fluctuations. J. Hydrol. 62, 129–141 (1983)

    Article  Google Scholar 

  6. Ahn, H.: Modeling of groundwater heads based on second-order difference time series models. J. Hydrol. 234, 82–94 (2000)

    Article  Google Scholar 

  7. Bidwell, V.J.: Realistic forecasting of groundwater level, based on the eigenstructure of aquifer dynamics. Math. Comput. Simul. 12–20 (2005)

    Google Scholar 

  8. Sudheer, Ch., Mathur, S.: Groundwater level forecasting using SVM-PSO. Int. J. Hydrol. Sci. Technol. 2, 202 (2012)

    Article  Google Scholar 

  9. Daliakopoulos, I.N., Coulibaly, P., Tsanis, I.K.: Ground water level forecasting using artificial neural networks. J. Hydrol. 309, 229–240 (2005)

    Article  Google Scholar 

  10. Shirmohammadi, B., Vafakhah, M., Moosavi, V., Moghaddamnia, A.: Application of several data-driven techniques for predicting groundwater level. Water Resour. Manag. 27, 419–432 (2013)

    Article  Google Scholar 

  11. Nourani, V., Ejlali, R.G., Alami, M.T.: Spatiotemporal Groundwater Level Forecasting in Coastal Aquifers by Hybrid Artificial Neural Network-Geostatistics Model: A Case Study (2011)

    Google Scholar 

  12. Raghavendra, N.S., Deka, P.C.: Forecasting monthly groundwater table fluctuations in coastal aquifers using Support vector regression. In: Anadinni, S. (ed.) International Multi Conference on Innovations in Engineering and Technology (IMCIET-2014), pp. 61–69. Elsevier Science and Technology, Bangalore (2014)

    Google Scholar 

  13. Shiri, J., Kisi, O., Yoon, H., Lee, K.-K., Nazemi, A.H.: Predicting groundwater level fluctuations with meteorological effect implications-{A} comparative study among soft computing techniques. Comput. Geosci. 56, 32–44 (2013)

    Article  Google Scholar 

  14. Yoon, H., Jun, S.-C., Hyun, Y., Bae, G.-O., Lee, K.-K.: A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. J. Hydrol. 396, 128–138 (2011)

    Article  Google Scholar 

  15. Suryanarayana, C., Sudheer, C., Mahammood, V., Panigrahi, B.K.: An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam, India. Neurocomputing 145, 324–335 (2014)

    Article  Google Scholar 

  16. Adamowski, J., Chan, H.F.: A wavelet neural network conjunction model for groundwater level forecasting. J. Hydrol. 407, 28–40 (2011)

    Article  Google Scholar 

  17. Maheswaran, R., Khosa, R.: Long term forecasting of groundwater levels with evidence of non-stationary and nonlinear characteristics. Comput. Geosci. 52, 422–436 (2013)

    Article  Google Scholar 

  18. Raghavendra, N.S., Deka, P.C.: Forecasting monthly groundwater level fluctuations in coastal aquifers using hybrid Wavelet packet—Support vector regression. Cogent Eng. 2, 999414 (2015)

    Google Scholar 

  19. Brahim-Belhouari, S., Bermak, A.: Gaussian process for nonstationary time series prediction (2004)

    Google Scholar 

  20. Sun, A.Y., Wang, D., Xu, X.: Monthly streamflow forecasting using Gaussian process regression. J. Hydrol. 511, 72–81 (2014)

    Article  Google Scholar 

  21. Grbić, R., Kurtagić, D., Slišković, D.: Stream water temperature prediction based on Gaussian process regression. Expert Syst. Appl. 40, 7407–7414 (2013)

    Article  Google Scholar 

  22. Roberts, S., Osborne, M., Ebden, M., Reece, S., Gibson, N., Aigrain, S.: Gaussian processes for time-series modelling. Philos. Trans. A. Math. Phys. Eng. Sci. 371, 20110550 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  23. Yan, W., Qiu, H., Xue, Y.: Gaussian process for long-term time-series forecasting. In: Proceedings of the International Joint Conference on Neural Networks, pp. 3420–3427 (2009)

    Google Scholar 

  24. Box, G.E.P.: Non-Normality and tests on variances. Biometrika 40, 318–335 (1953)

    Article  MathSciNet  MATH  Google Scholar 

  25. Rasmussen, C.E.: Gaussian processes in machine learning. In: Advanced Lectures on Machine Learning. Lecture Notes in Computer Science: Lecture Notes in Artificial Intelligence, pp. 63–71. Springer, Germany (2004)

    Google Scholar 

  26. Mackay, D.J.C.: Introduction to Gaussian processes. Neural Netw. Mach. Learn. 168, 133–165 (1998)

    MATH  Google Scholar 

  27. Rasmussen, C.E., Williams, C.: Gaussian processes for machine learning. Adaptive Computation and Machine Learning, p. 272. The MIT Press, Cambridge (2006)

    Google Scholar 

  28. Jang, J.S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23, 665–685 (1993)

    Article  Google Scholar 

  29. Keskin, M.E., Taylan, D., Terzi, Ö.: Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series. Hydrol. Sci. J. 51, 588–598 (2006)

    Article  Google Scholar 

  30. Hall, M., National, H., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11, 10–18 (2009)

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Department of Mines and Geology, Government of Karnataka for providing the necessary data required for research and the Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka for the necessary infrastructural support. The authors would like to thank four anonymous reviewers for their valuable suggestions and comments.

Author information

Authors and Affiliations

  1. Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Surathkal, Mangalore, 575025, India

    N. Sujay Raghavendra & Paresh Chandra Deka

Authors
  1. N. Sujay Raghavendra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paresh Chandra Deka
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to N. Sujay Raghavendra .

Editor information

Editors and Affiliations

  1. A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata, West Bengal, India

    Rituparna Chaki

  2. Computer Science, DAIS—Università Ca’ Foscari, Venice, Italy

    Agostino Cortesi

  3. Faculty of Computer Science, Bialystok University of Technology, Białystok, Poland

    Khalid Saeed

  4. Computer Science & Engineering, University of Calcutta, Kolkata, West Bengal, India

    Nabendu Chaki

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer India

About this chapter

Cite this chapter

Raghavendra, N.S., Deka, P.C. (2016). Multistep Ahead Groundwater Level Time-Series Forecasting Using Gaussian Process Regression and ANFIS. In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds) Advanced Computing and Systems for Security. Advances in Intelligent Systems and Computing, vol 396. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2653-6_19

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-2653-6_19

  • Published:

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2651-2

  • Online ISBN: 978-81-322-2653-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation