The accurate prediction of hydrological behaviour in both urban and rural watershed can provide valuable information for the urban planning, land use, design of civil project and water resources management. Hydrology system is influenced by many factors such as weather, land cover, infiltration, evapotranspiration, so it includes the good deal of stochastic dependent component, multi-time scale and highly nonlinear characteristics. Hydrologic time series are often nonlinear. In spite of high flexibility of Artificial Neural Network (ANN) in modelling hydrologic time series, sometimes signals exhibit seasonal irregularity. In such situation, ANN may not be able to cope with such data if pre-processing of input and/or output data is not performed. Pre-processing data refers to analysing and transforming input and output variables in order to detect trends, minimise noise, underline important relationship and flatten the variables distribution in a time series. These analysis and transformations help the model learn relevant patterns. Pre-processing techniques, which facilitates stabilisation of the mean and variance, and seasonality removal, are often applied to remove irregularities in data used to build soft computing models. In this study, different data pre-processing techniques are presented to deal with irregularity components existing in a hydrologic time series data of the Brahmaputra basin within India at the Pandu gauging station near Guwahati city using daily time unit and their properties are evaluated by performing one step ahead flow forecasting using ANN. The model results were evaluated using root mean square error (RMSE) and mean absolute percentage error (MAPE) and found that logarithmic-based pre-processing techniques provide better forecasting performance among various pre-processing techniques. The results indicate that detecting irregularities and selecting an appropriate pre-processing technique is highly beneficial in improving the prediction performance of ANN model.
[1]
Holger R. Maier,et al.
Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications
,
2000,
Environ. Model. Softw..
[2]
Bernd Freisleben,et al.
Nonstationarity and data preprocessing for neural network predictions of an economic time series
,
2000,
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.
[3]
Michael Y. Hu,et al.
Forecasting with artificial neural networks: The state of the art
,
1997
.
[4]
Leyan Xu,et al.
Short-term load forecasting techniques using ANN
,
2001,
Proceedings of the 2001 IEEE International Conference on Control Applications (CCA'01) (Cat. No.01CH37204).
[5]
Norbert Jankowski,et al.
Survey of Neural Transfer Functions
,
1999
.
[6]
Paresh Deka,et al.
Fuzzy Neural Network Model for Hydrologic Flow Routing
,
2005
.
[7]
Carlos R. Minussi,et al.
A fast electric load forecasting using neural networks
,
2000,
Proceedings of the 43rd IEEE Midwest Symposium on Circuits and Systems (Cat.No.CH37144).
[8]
C. W. Chan,et al.
A comparison of data preprocessing strategies for neural network modeling of oil production prediction
,
2004
.
[9]
Monica Lam,et al.
Neural network techniques for financial performance prediction: integrating fundamental and technical analysis
,
2004,
Decis. Support Syst..
[10]
Mohammad H. Aminfar,et al.
A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation
,
2009,
Eng. Appl. Artif. Intell..