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Long Term Streamflow Forecasting Using a Hybrid Entropy Model

Author

Listed:
  • A. B. Dariane

    (K.N. Toosi University of Technology)

  • M. Farhani

    (K.N. Toosi University of Technology)

  • Sh Azimi

    (K.N. Toosi University of Technology)

Abstract

In this paper, the development and evaluation of an entropy based hybrid data driven model coupled with input selection approach and wavelet transformation is investigated for long-term streamflow forecasting with 10 years lead time. To develop and test the models, data including 45 years of monthly streamflow time series from Taleghan basin, located in northwest of Tehran, are employed. For this purpose, first the performance of a maximum entropy forecasting model is evaluated. To boost the accuracy, an auto-correlation method with %95 confidence levels was carried out to determine the optimum order of the entropy model. Nevertheless, the basic entropy model, as expected, was only able to reach Nash-Sutcliffe efficiency (NSE) index of 0.35 during the test period. On the other hand, data driven models such as artificial neural networks (ANN) have shown to yield good accuracy in modeling complicated and nonlinear systems. Thus, to improve the performance of the maximum entropy model, an entropy-based hybrid model using evolutionary ANN (ENN) was proposed for further investigation. The proposed model with seasonality index substantially improved the test NSE to 0.51 and provided more accurate results than the basic entropy model. Moreover, when wavelet transform was applied to preprocess the input data, the model shows a slight improvement (NSE = 0.54).

Suggested Citation

  • A. B. Dariane & M. Farhani & Sh Azimi, 2018. "Long Term Streamflow Forecasting Using a Hybrid Entropy Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(4), pages 1439-1451, March.
    • Handle: RePEc:spr:waterr:v:32:y:2018:i:4:d:10.1007_s11269-017-1878-0
    DOI: 10.1007/s11269-017-1878-0
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    References listed on IDEAS

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    1. Arifovic, Jasmina & Gençay, Ramazan, 2001. "Using genetic algorithms to select architecture of a feedforward artificial neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 289(3), pages 574-594.
    2. Zaher Mundher Yaseen & Ozgur Kisi & Vahdettin Demir, 2016. "Enhancing Long-Term Streamflow Forecasting and Predicting using Periodicity Data Component: Application of Artificial Intelligence," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4125-4151, September.
    3. Alireza Dariane & Farzane Karami, 2014. "Deriving Hedging Rules of Multi-Reservoir System by Online Evolving Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(11), pages 3651-3665, September.
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    Cited by:

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    2. Xin Liu & Xuefeng Sang & Jiaxuan Chang & Yang Zheng, 2021. "Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4021-4041, September.

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