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Auto-Regressive Neural-Network Models for Long Lead-Time Forecasting of Daily Flow

Author

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  • Mohammad Ebrahim Banihabib

    (College Aburaihan, University of Tehran)

  • Reihaneh Bandari

    (College Aburaihan, University of Tehran)

  • Richard C. Peralta

    (Utah State University)

Abstract

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 / Q ¯ obs $$ \mathrm{RMSE}/{\overline{Q}}_{obs} $$ 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 / Q ¯ obs $$ \mathrm{RMSE}/{\overline{Q}}_{obs} $$ values of 0.616 in training and 0.678 in forecasting. The proposed model’s forecasting RMSE / Q ¯ obs $$ \mathrm{RMSE}/{\overline{Q}}_{obs} $$ is 20% lower than that of the ARIMA model.

Suggested Citation

  • Mohammad Ebrahim Banihabib & Reihaneh Bandari & Richard C. Peralta, 2019. "Auto-Regressive Neural-Network Models for Long Lead-Time Forecasting of Daily Flow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(1), pages 159-172, January.
  • Handle: RePEc:spr:waterr:v:33:y:2019:i:1:d:10.1007_s11269-018-2094-2
    DOI: 10.1007/s11269-018-2094-2
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    References listed on IDEAS

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    1. Noakes, Donald J. & McLeod, A. Ian & Hipel, Keith W., 1985. "Forecasting monthly riverflow time series," International Journal of Forecasting, Elsevier, vol. 1(2), pages 179-190.
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    Cited by:

    1. Mingxiang Yang & Hao Wang & Yunzhong Jiang & Xing Lu & Zhao Xu & Guangdong Sun, 2020. "GECA Proposed Ensemble–KNN Method for Improved Monthly Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 849-863, January.
    2. Mustafa Ozguven & Chong Yan Gao & Mohamed Yacine Si Tayeb, 2021. "The Utilization of Autoregressive Forecasting Models in Strategic Management," International Journal of Science and Business, IJSAB International, vol. 5(7), pages 170-185.
    3. Zhennan Liu & Qiongfang Li & Jingnan Zhou & Weiguo Jiao & Xiaoyu Wang, 2021. "Runoff Prediction Using a Novel Hybrid ANFIS Model Based on Variable Screening," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2921-2940, July.

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