Forecasting river flow rate during low‐flow periods using neural networks

The pollution in the river Arno downstream of the city of Florence is a severe environmental problem during low‐flow periods when the river flow rate is insufficient to support the natural waste assimilation mechanisms which include degradation, transport, and mixing. Forecasting the river flow rate during these low‐flow periods is crucial for water quality management. In this paper a neural network model is presented for forecasting river flow for up to 6 days. The model uses basin‐averaged rainfall measurements, water level, and hydropower production data. It is necessary to use hydropower production data since during low‐flow periods the water discharged into the river from reservoirs can be a major fraction of total flow rate. Model predictions were found to be accurate with root‐mean‐square error on the predicted river flow rate less then 8% over the entire time horizon of prediction. This model will be useful for managing the water quality in the river when employed with river quality models.

[1]  Kishan G. Mehrotra,et al.  Forecasting the behavior of multivariate time series using neural networks , 1992, Neural Networks.

[2]  Dennis McLaughlin,et al.  A space-time accurate method for solving solute transport problems , 1992 .

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

[4]  E. Clothiaux,et al.  Neural Networks and Their Applications , 1994 .

[5]  Mu-Lan Zhu,et al.  Application of Neural Networks to Runoff Prediction , 1994 .

[6]  Raymond Walton,et al.  QUAL2E SIMULATIONS OF PULSE LOADS , 1994 .

[7]  Luis Garrote,et al.  A distributed model for real-time flood forecasting using digital elevation models , 1995 .

[8]  Jason Smith,et al.  Neural-Network Models of Rainfall-Runoff Process , 1995 .

[9]  An application of neural nets to the Rome precipitation series (1782–1989) , 1995 .

[10]  D. Himmelblau,et al.  Identification of Nonlinear Dynamic Processes with Unknown and Variable Dead Time Using an Internal Recurrent Neural Network , 1995 .

[11]  H. Raman,et al.  Multivariate modelling of water resources time series using artificial neural networks , 1995 .

[12]  Kuolin Hsu,et al.  Artificial Neural Network Modeling of the Rainfall‐Runoff Process , 1995 .

[13]  Roland K. Price,et al.  A neural network model of rainfall-runoff using radial basis functions , 1996 .

[14]  M. J. Hall,et al.  Artificial neural networks as rainfall-runoff models , 1996 .

[15]  J. Refsgaard,et al.  Operational Validation and Intercomparison of Different Types of Hydrological Models , 1996 .

[16]  E. Todini The ARNO rainfall-runoff model , 1996 .

[17]  Estimation of flood forecasting : Errors and flow-duration joint probabilities of exceedance , 1996 .

[18]  H. Maier,et al.  The Use of Artificial Neural Networks for the Prediction of Water Quality Parameters , 1996 .

[19]  Runoff components simulated by rainfallrunoff models , 1996 .

[20]  David A. Woolhiser,et al.  Search for physically based Runoff model : A hydrologic El Dorado ? , 1996 .

[21]  A. Shamseldin Application of a neural network technique to rainfall-runoff modelling , 1997 .

[22]  A. Soldati,et al.  River flood forecasting with a neural network model , 1999 .