Indian Journal of Agricultural Research

  • Chief EditorT. Mohapatra

  • Print ISSN 0367-8245

  • Online ISSN 0976-058X

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  • SJR 0.293

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Indian Journal of Agricultural Research, volume 54 issue 1 (february 2020) : 27-34

Comparative Study between Wavelet Artificial Neural Network (WANN) and Artificial Neural Network (ANN) Models for Groundwater Level Forecasting

Anandakumar, B. Maheshwara Babu, U. Satishkumar, G.V. Srinivasa Reddy
1Department of Soil and Water Engineering, College of Agricultural Engineering, Raichur-584 104, Karnataka, India.
Cite article:- Anandakumar, Babu Maheshwara B., Satishkumar U., Reddy Srinivasa G.V. (2019). Comparative Study between Wavelet Artificial Neural Network (WANN) and Artificial Neural Network (ANN) Models for Groundwater Level Forecasting. Indian Journal of Agricultural Research. 54(1): 27-34. doi: 10.18805/IJARe.A-5079.
Groundwater level fluctuation modeling is a prime need for effective utilization and planning the conjunctive use in any basin.The application of Artificial Neural Network (ANN) and hybrid Wavelet ANN (WANN) models was investigated in predicting Groundwater level fluctuations. The RMSE of ANN model during calibration and validation were found to be 0.2868 and 0.3648 respectively, whereas for the WANN model the respective values were 0.1946 and 0.1695. Efficiencies during calibration and validation for ANN model were 0.8862 per cent and 0.8465 per cent respectively, whereas for WANN model were found to be much higher with the respective values of 0.9436 per cent and 0.9568 per cent indicating substantial improvement in the model performance. Hence hybrid ANN model is the promising tool to predict water table fluctuation as compared to ANN model. 
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