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Prediction of Urmia Lake Water-Level Fluctuations by Using Analytical, Linear Statistic and Intelligent Methods

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Abstract

Undoubtedly, the most significant factor with wise decision making and designing hydrological structures along the lake coasts is an accurate model of lake level changes. This issue becomes more and more important as recent global climate changes have completely reformed the behavior of traditional lake level fluctuations. Subsequently, estimating lake levels becomes more important and at the same time more difficult. This paper deals with modeling lake level changes of Lake Urmia located in north-west of Iran, in terms of both simulator and predictor models. According to this, two traditional simulator models based on water budget are developed which benefit from most effective components on water budget namely precipitation, evaporation, inflow and the lake level antecedents, as model inputs. Most famous linear modeling tools, Autoregressive with exogenous input (ARX) and Box-Jenkins (BJ) models are employed with the same mentioned inputs for prediction purpose. In addition, two other methods that are, Multi-Layer Perceptron (MLP) neural network and also Local Linear Neuro-Fuzzy (LLNF) are applied to investigate capability of intelligent nonlinear methods for lake level changes prediction. All models performances are indicated using both graph and numerical illustrations and results are discussed. Comparative results reveal that the intelligent methods are superior to traditional models for modeling lake level behavior as complex hydrological phenomena.

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Acknowledgments

First author would like to thank Ms. Mina Ghanavati for her help and feedback. The authors express their thanks to Dr. Mahdi Aliyari Shooredeli for his scientific recommendations. We also sincerely appreciate Dr. Ellips Masehian for his valuable guidance.

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Authors and Affiliations

  1. Sama Technical and Vocational Training College, Islamic Azad University, Tehran Branch, Tehran, Iran

    Hossein Kakahaji

  2. Department of Electrical Engineering, Islamic Azad University, Ashkezar Branch, Yazd, Iran

    Hamed Dehghan Banadaki

  3. Department of Civil Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran

    Abbas Kakahaji

  4. Water Resources Management Company of Iran, Tehran, Iran

    Abdulamir Kakahaji

Authors
  1. Hossein Kakahaji
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  2. Hamed Dehghan Banadaki
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  3. Abbas Kakahaji
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  4. Abdulamir Kakahaji
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Correspondence to Hossein Kakahaji.

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Kakahaji, H., Banadaki, H.D., Kakahaji, A. et al. Prediction of Urmia Lake Water-Level Fluctuations by Using Analytical, Linear Statistic and Intelligent Methods. Water Resour Manage 27, 4469–4492 (2013). https://doi.org/10.1007/s11269-013-0420-2

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  • DOI: https://doi.org/10.1007/s11269-013-0420-2

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