Abstract
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.
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References
Cannas B, Fanni A, Sias G, Tronchi S, Zedda MK (2005) River flow forecasting using Neural Networks and Wavelet Analysis, vol 7. EUG, European Geosciences Union, Vienna, Austria, pp 24–29
Deka P, Chandramoulli V (2005) Fuzzy neural network modelling for hydrologic flow routing. ASCE J Hydrol Eng 10(4):302–314
Duch W, Jankowski N (1999) Survey of neural transfer functions. Neural Comput Surv 2:163–212
Kong JHL, Martin GPMD (1995) A back propagation neural network for sales forecasting. IEEE 2121–2124
Lam M (2004) Neural network techniques for fincial performance prediction:integrating fundamental and technical analysis. Decis Support Syst 37:567–581
Lopes MLM, Minussi CR, Lotufo ADP (2000) A fast electric load forecasting using neural networks. In: Proceedings 43rd IEEE Midwest symposium on circuits and systems, learning MI. 8–11 Aug 2000, IEEE, pp 1–4
Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw 15:101–124
Nguyen HH, Chan CW (2004) A comparison of data preprocessing strategies for neural network modelling of oil production prediction. In: Proceedings of the third IEEE International conference and cognitive informatics (ICCI’04). IEEE Computer Science
Nourani V, Alami MT, Aminfar MH (2009) A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Elsevier, Eng Appl Artif Intell 22:466–472
Plummer EA (2000) Time series forecasting with feedforward neural networks: guidelines and limitations. Master thesis, University of Wyoming
Sreenivasulu D, Deka PC (2011) A comparative study on RBF and NARX based methods for forecasting of groundwater level. Int J Earth Sci Eng 04(4):743–756
Virili F, Freisleben B (2000) Nonstationary and data preprocessing for neural network predictions of an economic time series. IEEE 129–134
Xu L, Chen WJ (2001) Short term load forecasting techniques using ANN. In: Proceedings of the 2001 IEEE International Conference of control Applications, pp 157–160
Zhang G, Patuwo BE, Michael YH (1998) Forecasting with artificial neural networks. IEEE 909–912
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Banhatti, A.G., Deka, P.C. (2016). Effects of Data Pre-processing on the Prediction Accuracy of Artificial Neural Network Model in Hydrological Time Series. In: Sarma, A., Singh, V., Kartha, S., Bhattacharjya, R. (eds) Urban Hydrology, Watershed Management and Socio-Economic Aspects. Water Science and Technology Library, vol 73. Springer, Cham. https://doi.org/10.1007/978-3-319-40195-9_21
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DOI: https://doi.org/10.1007/978-3-319-40195-9_21
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