Performance Evaluation of Artificial Neural Network Model Using Data Preprocessing in Non-Stationary Hydrologic Time Series

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.