Deakin University
Browse

File(s) under permanent embargo

Auto-regressive neural-network models for long lead-time forecasting of daily flow

journal contribution
posted on 2019-01-01, 00:00 authored by Mohammad Ebrahim Banihabib, Reihaneh Bandari, Richard C Peralta
Accurate reservoir-inflow forecasting is especially important for optimizing operation of multi-propose reservoirs that provide hydropower generation, flood control, and water for domestic use and irrigation. There are no previous reports of successful daily flow prediction using a 1-year lead-time. This paper reports successful daily stream flow predictions for that extended lead-time. It presents the first NARX (Nonlinear Auto Regressive model with eXogenous inputs)-type recurrent neural network (NARX-RNN) model used to forecast daily reservoir inflow for a long lead-time. It is the first use of dynamic memory to extend the forecast lead-time beyond the previously reported 1-week lead-times. For new nonlinear NARX-RNN models, we present and test 1600 alternative structures, differing in transfer functions (2), and numbers of inputs (2 to 5), neurons per hidden layer (1 to 20), input delays and output delays. For predicting inflow to the reservoir of the multi-purpose Dez Dam, we contrast accuracies of forecasts from the new models, and from a conventional auto-regressive linear ARIMA model. Based upon normalized root-mean-square error RMSE/π‘„βŽ―βŽ―βŽ―βŽ―βŽ―π‘œπ‘π‘  the best NARX-RNN has log-sigmoid transfer functions, three inputs, one hidden layers, four neurons in the hidden layer, two input delays, and 10 output delays. That NARX-RNN structure yields RMSE/π‘„βŽ―βŽ―βŽ―βŽ―βŽ―π‘œπ‘π‘  values of 0.616 in training and 0.678 in forecasting. The proposed model’s forecasting RMSE/π‘„βŽ―βŽ―βŽ―βŽ―βŽ―π‘œπ‘π‘  is 20% lower than that of the ARIMA model.

History

Journal

Water resources management

Volume

33

Pagination

159 - 172

Publisher

Springer

Location

Dordrecht, The Netherlands

ISSN

0920-4741

eISSN

1573-1650

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Usage metrics

    Research Publications

    Categories

    No categories selected

    Keywords

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC