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Gumageri, Nagaraj
- Evaluation of GRNN and RBF Model Performance for Groundwater Level Forecasting at Southwest Coast of India
Authors
1 Department of Civil Engineering, Sree Vidyanikethan Engineering College, Sree Sainath Nagar, Tirupati, A. Rangampet-517102, Andhra Pradesh, IN
2 Department of Civil Engineering, Bearys Institute of Technology, Innoli, Boliyar Village, Near Mangalore University, Mangalore – 574153, Karnataka, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 5, No 8 (2013), Pagination: 359-365Abstract
An accurate and reliable forecast plays a vital role for proper planning and utilization of groundwater resources in a sustainable manner. In the present work, an investigation has been carried out in selective wells based on different land use/land cover in the micro watershed located southwest coast of India. The present study utilizes the Generalized Regression Neural Network (GRNN) for forecasting groundwater level (GWL) and compares its performance with that of the Feed Forward Back Propagation (FFBP) trained with Levenberg Marquartz (LM)] and Radial Basis Function (RBF). Weekly time series groundwater level data were used for span of three years (2004-2007). The comparative analysis of the obtained results showed that the GRNN and RBF have the superiority over the FFBP methods for forecasting groundwater level. On the basis of performance criteria (i.e lower RMSE and higher CE), GRNN yielded the better performance to RBF considering the models developed in the study.
Keywords
ANN, FFBP, GRNN, GWL, RBF.
- Investigation of the Effects of Meteorological Parameters on Groundwater Level using ANN
Authors
1 Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Surathkal-575025, Karnataka (D.K), IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 4, No 1 (2012), Pagination: 39-44Abstract
In the present research the effect of meteorological parameters such as temperature, relative humidity, evaporation and rainfall on groundwater level fluctuation has been investigated for Dakshina Kannada coastal aquifer at southwest coast of India. Weekly time series meteorological data were used for a span of three years (2004-2007). Generalized regression neural network (GRNN) and feed-forward back propagation networks (FFBP) were employed to develop various models. Model Input combinations were selected based on autocorrelation. The performances of developed models were evaluated using performance indices such as ischolar_main mean square error (RMSE) and coefficient of efficiency (CE). The obtained results showed closed relationship between rainfall event and groundwater level during monsoon. It was also, observed that the temperature and evaporation had significant effect on groundwater level fluctuations in non-monsoon season. The obtained GRNN results were compared with that of FFBP. A better agreement was observed between the actual and modeled groundwater levels for GRNN than that of FFBP. From the study, GRNN can be applied successfully for forecasting groundwater level due to its accuracy and reliable results.