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Deka, Paresh Chandra
- Performance Evaluation of Artificial Neural Network Model Using Data Preprocessing in Non-Stationary Hydrologic Time Series
Authors
1 Department of Applied Mechanics, National Institute of Technology, Surathkal, Karnataka, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 4, No 4 (2012), Pagination: 223-229Abstract
For the planning, land use, design of civil projects and water resources management, the accurate prediction of hydrological behaviour in the watershed can provide valuable information. Hydrologic systems include, to a large extent, stochastic components and are often non-linear and non-stationary. Inspite of high adaptability of Artificial Neural Network (ANN) in modelling hydrologic time series, often signals are highly non-stationary and exhibit seasonal irregularity. In such cases, prediction accuracy of ANN suffers for want of pre-processing of data. In this study, different data pre-processing techniques are presented to deal with irregularity components that exist in hydrologic time series data of the Brahmaputra basin within India at the Pancharatna gauging station using daily time unit and their properties are evaluated by performing one step ahead flow forecasting using ANN. The model results are evaluated by using Root mean square error (RMSE) and Mean absolute percentage error (MAPE) and it was found that Logarithm based pre-processing technique provides better forecasting performance among various pre-processing techniques. The results indicate that detecting non-stationary nature and selecting an appropriate pre-processing technique is highly beneficial in improving the prediction performance of ANN model.Keywords
ANN, Non-Stationary, Data Pre-Processing, Activation Function, Time Series.- 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.