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Optimized River Stream-Flow Forecasting Model Utilizing High-Order Response Surface Method

Author

Listed:
  • Behrooz Keshtegar

    (University of Zabol)

  • Mohammed Falah Allawi

    (Universiti Kebangsaan Malaysia)

  • Haitham Abdulmohsin Afan

    (Universiti Kebangsaan Malaysia)

  • Ahmed El-Shafie

    (University Malaya)

Abstract

Accurate and reliable stream-flow forecasting has a key role in water resources planning and management. Most recently, soft computing approaches have become progressively prevalent in modelling hydrological variables and most specifically stream-flows. This is due to their ability to capture the non-linearity and non-stationarity characteristics of the hydrological variables with minimum information requirements. Despite this, they present several challenges in the modelling architecture, as there is a need to establish a suitable pre-processing method for the stream-flow data and an appropriate optimization model has to be integrated in order re-adjust the weights and biases associated with the model structure. On top of that, artificial intelligent models require “trial and error” procedures in order to be properly tuned (number of hidden layers, number of neurons within the hidden layers and the type of the transfer function). However, soft computing approach experienced several problems while calibration such as over-fitting. In this research, the Response Surface Method (RSM) is improved based on high-order polynomial functions for forecasting the river stream-flow namely; High-Order Response Surface (HORS) method. Several higher orders have been examined, second, third, fourth and fifth polynomial functions in order to figure out the best fit that able to mimic the pattern of stream-flow. In order to demonstrate the effectiveness of the proposed model, monthly stream-flow time series data located in Aswan High Dam (AHD) has been examined. A detailed analysis of the overall statistical indicators revealed that the proposed method showed outstanding performance for monthly stream-flow forecasting at AHD. It could be concluded that the fifth order polynomial function outperforms the other orders of the polynomial functions especially with May model who achieved minimum MAE 0.12, NRMSE 0.07, MSE 0.03 and maximum SF and R2 (0.97, 0.99) respectively.

Suggested Citation

  • Behrooz Keshtegar & Mohammed Falah Allawi & Haitham Abdulmohsin Afan & Ahmed El-Shafie, 2016. "Optimized River Stream-Flow Forecasting Model Utilizing High-Order Response Surface Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(11), pages 3899-3914, September.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:11:d:10.1007_s11269-016-1397-4
    DOI: 10.1007/s11269-016-1397-4
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    References listed on IDEAS

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    1. Ahmed El-Shafie & Alaa Abdin & Aboelmagd Noureldin & Mohd Taha, 2009. "Enhancing Inflow Forecasting Model at Aswan High Dam Utilizing Radial Basis Neural Network and Upstream Monitoring Stations Measurements," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(11), pages 2289-2315, September.
    2. Hadi Sanikhani & Ozgur Kisi, 2012. "River Flow Estimation and Forecasting by Using Two Different Adaptive Neuro-Fuzzy Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(6), pages 1715-1729, April.
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    2. Yutao Qi & Zhanao Zhou & Lingling Yang & Yining Quan & Qiguang Miao, 2019. "A Decomposition-Ensemble Learning Model Based on LSTM Neural Network for Daily Reservoir Inflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(12), pages 4123-4139, September.
    3. Hai Tao & Behrooz Keshtegar & Zaher Mundher Yaseen, 2019. "The Feasibility of Integrative Radial Basis M5Tree Predictive Model for River Suspended Sediment Load Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(13), pages 4471-4490, October.
    4. Majid Dehghani & Hossein Riahi-Madvar & Farhad Hooshyaripor & Amir Mosavi & Shahaboddin Shamshirband & Edmundas Kazimieras Zavadskas & Kwok-wing Chau, 2019. "Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 12(2), pages 1-20, January.
    5. Asim Jahangir Khan & Manfred Koch & Adnan Ahmad Tahir, 2020. "Impacts of Climate Change on the Water Availability, Seasonality and Extremes in the Upper Indus Basin (UIB)," Sustainability, MDPI, vol. 12(4), pages 1-27, February.
    6. Keshtegar, Behrooz & Mert, Cihan & Kisi, Ozgur, 2018. "Comparison of four heuristic regression techniques in solar radiation modeling: Kriging method vs RSM, MARS and M5 model tree," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 330-341.

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