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A Hybrid-Wavelet Artificial Neural Network Model for Monthly Water Table Depth Prediction


Affiliations
1 Department of Soil and Water Engineering, College of Agricultural Engineering, Raichur 584 104, India
2 National Institute of Hydrology, Roorkee 247 667, India
3 Western Himalayan Regional Centre, National Institute of Hydrology, Jammu 180 003, India
 

Groundwater is an essential natural resource in the country to fulfil the irrigation, domestic, industrial and other needs. In order to ensure sustainable use of groundwater resources, the groundwater level is used as an important indicator for balancing the groundwater withdrawal rate and replenishment rate through the recharge. Quantitatively, the recharge rate is governed by various complex large-scale hydrological processes and hence achievement of sustainability of groundwater supplies, through sustainable withdrawal rate is a complicated issue. In the present study, a data-driven prediction model by combining discrete wavelet transform (DWT) with artificial neural network (ANN) called as wavelet artificial neural network (WANN) is proposed for the groundwater table prediction. The simulation results obtained by regular ANN model were compared with those obtained by WANN model to prove the superiority of the latter model over the former. WANN model was developed using decomposed signals of rainfall, evapotranspiration and water table depth time series as inputs in the ANN model to arrive at a prediction of monthly fluctuation of the groundwater table. Rainfall time series was decomposed using Haar wavelet at third decomposition level and evapotranspiration and water table depth time series was decomposed using Daubechies wavelet at second decomposition level. The RMSE value of ANN and WANN model during validation were found to be 0.3648 m and 0.1695 m respectively, which showed decrease in RMSE value by 0.195 m when WANN was applied. Model efficiencies of ANN and WANN model during validation were 84.65% and 95.68%, indicating excellent improvement of model accuracy after applying WANN. Hence, the proposed WANN model seems to be a promising tool to predict the monthly water table fluctuation.

Keywords

Artificial Neural Network, Wavelet Transformation, Wavelet Artificial Neural Network, Water Table Depth Prediction.
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  • French, M. N., Krajewski, W. F. and Cuykendall, R. R., Rainfall forecasting in space and time using a neural network. J. Hydrol., 1992, 137, 1–31.

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

  • Krishna, B., Satyaji Rao, Y. R. and Vijaya, T., Modelling groundwater levels in an urban coastal aquifer using artificial neural networks. Hydrol. Process, 2008, 22, 1180–1188.

  • Nakhaei, M. and Saberi Nasr, A., A combined wavelet-artificial neural network model and its application to the prediction of groundwater level fluctuations. J. Geope., 2012, 2(2), 77–91.

  • Vetrivel. N. and Elangovan. K., Prediction and forecasting of groundwater level fluctuation by ANN technique. Int. J. Civil Eng. Technol., 2016, 7(5), 401–408.

  • Rhif, M., Abbes, A. B., Farah, I. R., Martinez, B. and Sang, Y., Wavelet transform application for/in non-stationary time-series analysis: a review. Appl. Sci., 2019, 9, 1345.

  • Zhou, H. C., Peng, Y. and Liang, G. H., The research of monthly discharge predictor-corrector model based on wavelet decomposition. Water Resour. Manage., 2008, 22, 217–227.

  • Nourani, V. and Parhizkar, M., Conjunction of SOM-based feature extraction method and hybrid wavelet-ANN approach for rainfall– runoff modeling. J. Hydroinform., 2013, 15(3), 829–848.

  • Grossmann, A. and Morlet, J., Decomposition of Hardy function into square integrable wavelets of constant shape. J. Math. Anal., 1984, 15(4), 723–736.

  • Adamowski, J. and Sun, K., Development of a coupled wavelet transform and neural network method for flow forecasting of nonperennial rivers in semi-arid watersheds. J. Hydrol., 2010, 390 (1–2), 85–91.

  • Kisi, O., Neural networks and wavelet conjunction model for intermittent stream-flow forecasting. J. Hydrol. Eng., 2009, 14(8), 773–782.

  • Kisi, O., Neural network and wavelet conjunction model for modeling monthly level fluctuations in Turkey. Hydrol. Process., 2009, 23(14), 2081–2092.

  • Nourani, V., Kisi, O. and Komasi, M., Two hybrid artificial intelligence approaches for modeling rainfall–runoff process. J. Hydrol., 2011, 402(1–2), 41–59.

  • Maheswaran, R. and Khosa, R., Comparative study of different wavelets for hydrologic forecasting. Comput. Geosci., 2012, 46, 284–295.

  • Maheswaran, R. and Khosa, R. Wavelet-Volterra coupled model for monthly stream flow forecasting. J. Hydrol., 2012, 450, 320–335.

  • Sang, Y. F., A practical guide to discrete wavelet decomposition of hydrologic time series. Water Resour. Manage., 2012, 26(11), 3345–3365.

  • Tiwari, M. K. and Chatterjee, C., A new wavelet-bootstrap-ANN hybrid model for daily discharge forecasting. J. Hydroinform., 2011, 13(3), 500–519.

  • Bhabagrahi, S., Imtisenla, W., Bidyut, C. D. and Bhagwati, P. B., Standardization of reference evapotranspiration models for a sub humid valley rangeland in the Eastern Himalayas. J. Irrig. Drain Eng., 2012, 138, 880–895.

  • Fausett, L., Fundamentals of Neural Networks, Prentice Hall, Englewood Cliffs, NJ, 1994.

  • Maier, H. R. and Dandy, G. C., Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ. Modell. Softw., 2000, 15, 101– 124.

  • Anctil, F., Perrin, C. and Andreassian, V., Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models. Environ. Modeling Software, 2004, 19(4), 357–368.

  • Porter, D. W., Gibbs, P. G., Jones, W. F., Huyakorn, P. S., Hamm, L. L. and Flach, G. P., Data fusion modeling for groundwater systems. J. Contaminant Hydrol., 2000, 42, 303–335.

  • Mallat, S., A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell., 1989, 11, 674–693.

  • Cannas, B., Fanni, A., See, L. and Sias, G., Data pre-processing for river flow forecasting using neural networks: wavelet transforms and data partitioning. Phys. Chem. Earth., 2005, 31, 1164– 1171.

  • Chandrasekhar, E., Dimri, V. P. and Gadre, V. M., Wavelets and Fractals in Earth System Sciences, CRC Press, Taylor and Francis, UK, 2013.

  • Nourani, V., Komasi, M. and Mano, A., A multivariate ANN Wavelet approach for rainfall-runoff modeling. Water Resour. Manage., 2009, 23, 2877–2894.


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  • A Hybrid-Wavelet Artificial Neural Network Model for Monthly Water Table Depth Prediction

Abstract Views: 436  |  PDF Views: 136

Authors

Anandakumar
Department of Soil and Water Engineering, College of Agricultural Engineering, Raichur 584 104, India
A. R. Senthil Kumar
National Institute of Hydrology, Roorkee 247 667, India
Ravindra Kale
Western Himalayan Regional Centre, National Institute of Hydrology, Jammu 180 003, India
B. Maheshwara Babu
Department of Soil and Water Engineering, College of Agricultural Engineering, Raichur 584 104, India
U. Sathishkumar
Department of Soil and Water Engineering, College of Agricultural Engineering, Raichur 584 104, India
G. V. Srinivasa Reddy
Department of Soil and Water Engineering, College of Agricultural Engineering, Raichur 584 104, India
Prasad S. Kulkarni
Department of Soil and Water Engineering, College of Agricultural Engineering, Raichur 584 104, India

Abstract


Groundwater is an essential natural resource in the country to fulfil the irrigation, domestic, industrial and other needs. In order to ensure sustainable use of groundwater resources, the groundwater level is used as an important indicator for balancing the groundwater withdrawal rate and replenishment rate through the recharge. Quantitatively, the recharge rate is governed by various complex large-scale hydrological processes and hence achievement of sustainability of groundwater supplies, through sustainable withdrawal rate is a complicated issue. In the present study, a data-driven prediction model by combining discrete wavelet transform (DWT) with artificial neural network (ANN) called as wavelet artificial neural network (WANN) is proposed for the groundwater table prediction. The simulation results obtained by regular ANN model were compared with those obtained by WANN model to prove the superiority of the latter model over the former. WANN model was developed using decomposed signals of rainfall, evapotranspiration and water table depth time series as inputs in the ANN model to arrive at a prediction of monthly fluctuation of the groundwater table. Rainfall time series was decomposed using Haar wavelet at third decomposition level and evapotranspiration and water table depth time series was decomposed using Daubechies wavelet at second decomposition level. The RMSE value of ANN and WANN model during validation were found to be 0.3648 m and 0.1695 m respectively, which showed decrease in RMSE value by 0.195 m when WANN was applied. Model efficiencies of ANN and WANN model during validation were 84.65% and 95.68%, indicating excellent improvement of model accuracy after applying WANN. Hence, the proposed WANN model seems to be a promising tool to predict the monthly water table fluctuation.

Keywords


Artificial Neural Network, Wavelet Transformation, Wavelet Artificial Neural Network, Water Table Depth Prediction.

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DOI: https://doi.org/10.18520/cs%2Fv117%2Fi9%2F1475-1481