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

Autor:	B. Maheshwara Babu, Anandakumar, U. Satishkumar, G. V. Srinivasa Reddy
Rok vydání:	2019
Předmět:	Wavelet Calibration and validation Mean squared error Artificial neural network Statistics Soil Science Plant Science Horticulture Conjunctive use Agronomy and Crop Science Groundwater
Zdroj:	Indian Journal Of Agricultural Research.
ISSN:	0976-058X 0367-8245
DOI:	10.18805/ijare.a-5079
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::63e39c4b61e7c44725855036132f1635 https://doi.org/10.18805/ijare.a-5079 Zobrazit plný text záznamu