NARX Recurrent Neural-Network Model for Long-Term Forecasting of Daily flow
Location
Eccles Conference Center
Event Website
http://water.usu.edu
Start Date
4-1-2014 7:05 PM
End Date
4-1-2014 7:10 PM
Description
Forecasted river flow can be utilized to optimally allocate water to agriculture, municipal, and industrial demands for the coming year. This research forecasts long-term daily inflow into Dez Reservoir. To develop a NARX Recurrent Neural Network (NARX-RNN) model, we evaluate different input delays, number of neurons in the hidden layer, types of transfer functions, and different numbers of output delays. The best NARX-RNN model, based upon root mean square error (RMSE) and mean bias error (MBE), uses a log-sigmoid transfer function, 4 neurons in the hidden layer and 10 outputs delay. We also use an Auto Regressive Integrated Moving Average (ARIMA) model which uses Fourier series parameters to eliminate seasonal trend. Training and testing both models employs daily discharge data from 1975 to 2011. Comparison of observed and forecasted reservoir inflow results shows that NARX-RNN operates more successfully than ARIMA model in 365-day daily inflow forecasting, especially for peak flows.
NARX Recurrent Neural-Network Model for Long-Term Forecasting of Daily flow
Eccles Conference Center
Forecasted river flow can be utilized to optimally allocate water to agriculture, municipal, and industrial demands for the coming year. This research forecasts long-term daily inflow into Dez Reservoir. To develop a NARX Recurrent Neural Network (NARX-RNN) model, we evaluate different input delays, number of neurons in the hidden layer, types of transfer functions, and different numbers of output delays. The best NARX-RNN model, based upon root mean square error (RMSE) and mean bias error (MBE), uses a log-sigmoid transfer function, 4 neurons in the hidden layer and 10 outputs delay. We also use an Auto Regressive Integrated Moving Average (ARIMA) model which uses Fourier series parameters to eliminate seasonal trend. Training and testing both models employs daily discharge data from 1975 to 2011. Comparison of observed and forecasted reservoir inflow results shows that NARX-RNN operates more successfully than ARIMA model in 365-day daily inflow forecasting, especially for peak flows.
https://digitalcommons.usu.edu/runoff/2014/2014Posters/1