A comparison between neural-network forecasting techniques-case study: river flow forecasting

Estimating the flows of rivers can have significant economic impact, as this can help in agricultural water management and in protection from water shortages and possible flood damage. The first goal of this paper is to apply neural networks to the problem of forecasting the flow of the River Nile in Egypt. The second goal of the paper is to utilize the time series as a benchmark to compare between several neural-network forecasting methods.We compare between four different methods to preprocess the inputs and outputs, including a novel method proposed here based on the discrete Fourier series. We also compare between three different methods for the multistep ahead forecast problem: the direct method, the recursive method, and the recursive method trained using a backpropagation through time scheme. We also include a theoretical comparison between these three methods. The final comparison is between different methods to perform longer horizon forecast, and that includes ways to partition the problem into the several subproblems of forecasting K steps ahead.

[1]  Andreas S. Weigend,et al.  Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .

[2]  Paul J. Werbos,et al.  Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.

[3]  Les E. Atlas,et al.  Recurrent neural networks and robust time series prediction , 1994, IEEE Trans. Neural Networks.

[4]  Z. H. Ashour,et al.  Time Series Models Adoptable for Forecasting Nile Floods and Ethiopian Rainfalls. , 1994 .

[5]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[6]  Juan B. Valdés,et al.  Streamflow Forecasting for Han River Basin, Korea , 1994 .

[7]  A. Weigend,et al.  Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .

[8]  Michael I. Jordan,et al.  A Competitive Modular Connectionist Architecture , 1990, NIPS.

[9]  Richard D. Braatz,et al.  On the "Identification and control of dynamical systems using neural networks" , 1997, IEEE Trans. Neural Networks.

[10]  H. E. Hurst,et al.  Long-Term Storage Capacity of Reservoirs , 1951 .

[11]  Edward A. McBean,et al.  River Flow Forecasting Model For Sturgeon River , 1985 .

[12]  Robert J. Marks,et al.  Electric load forecasting using an artificial neural network , 1991 .

[13]  Richard J. Heggen,et al.  Neural Networks for River Flow Prediction , 1995 .

[14]  I. Rodríguez‐Iturbe,et al.  Random Functions and Hydrology , 1984 .

[15]  Ashok N. Srivastava,et al.  Nonlinear gated experts for time series: discovering regimes and avoiding overfitting , 1995, Int. J. Neural Syst..

[16]  Yuan Cheng.  Evaluating an Autoregressive Model for Stream Flow Forecasting , 1994 .