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Comparison of FFNN and ANFIS models for estimating groundwater level

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Abstract

Prediction of water level is an important task for groundwater planning and management when the water balance consistently tends toward negative values. In Maheshwaram watershed situated in the Ranga Reddy District of Andhra Pradesh, groundwater is overexploited, and groundwater resources management requires complete understanding of the dynamic nature of groundwater flow. Yet, the dynamic nature of groundwater flow is continually changing in response to human and climatic stresses, and the groundwater system is too intricate, involving many nonlinear and uncertain factors. Artificial neural network (ANN) models are introduced into groundwater science as a powerful, flexible, statistical modeling technique to address complex pattern recognition problems. This study presents the comparison of two methods, i.e., feed-forward neural network (FFNN) trained with Levenberg–Marquardt (LM) algorithm compared with a fuzzy logic adaptive network-based fuzzy inference system (ANFIS) model for better accuracy of the estimation of the groundwater levels of the Maheshwaram watershed. The statistical indices used in the analysis were the root mean square error (RMSE), regression coefficient (R 2) and error variation (EV).The results show that FFNN-LM and ANFIS models provide better accuracy (RMSE = 4.45 and 4.94, respectively, R 2 is 93% for both models) for estimating groundwater levels well in advance for the above location.

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Acknowledgments

We are grateful to the Director, NGRI (CSIR), Hyderabad, for encouragement and permission to publish this work. The senior author thanks the ICAR, New Delhi, for sanctioning study leave. The second author gratefully acknowledges the Council of Scientific and Industrial Research (CSIR), New Delhi, for financial assistance and also the anonymous reviewers who provided valuable suggestions for the improvement of the manuscript.

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Authors and Affiliations

  1. Computer Science, Directorate of Cashew Research, Puttur, Karnataka, India

    P. D. Sreekanth

  2. IFCGR, National Geophysical Research Institute (CSIR), Hyderabad, India

    P. D. Sreedevi & Shakeel Ahmed

  3. Department of Computer Science, Sri Krishnadevaraya University, Anantapur, India

    N. Geethanjali

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  1. P. D. Sreekanth
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  2. P. D. Sreedevi
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  3. Shakeel Ahmed
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  4. N. Geethanjali
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Correspondence to P. D. Sreedevi.

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Sreekanth, P.D., Sreedevi, P.D., Ahmed, S. et al. Comparison of FFNN and ANFIS models for estimating groundwater level. Environ Earth Sci 62, 1301–1310 (2011). https://doi.org/10.1007/s12665-010-0617-0

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  • DOI: https://doi.org/10.1007/s12665-010-0617-0

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