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Prediction of Water Level using Monthly Lagged Data in Lake Urmia, Iran

Author

Listed:
  • Babak Vaheddoost

    (Istanbul Technical University)

  • Hafzullah Aksoy

    (Istanbul Technical University)

  • Hirad Abghari

    (Urmia University)

Abstract

Prediction of water level fluctuations in lakes is a necessary task in hydrological and limnological studies. Lake Urmia, a hyper-saline lake in the North Western part of Iran, is dealing with a gradual atrophy. In this study, parametric and nonparametric models are used for predicting monthly water level fluctuations in Lake Urmia. Eleven previous water levels in the form of monthly lagged data are used as the known independent variables of the model while lake water level at the twelfth month is considered as the unknown dependent variable to be predicted. Parametric models used in the modelling are multi-linear regression (MLR), additive and multiplicative non-linear regression (ANLR and MNLR) and decision tree (DT) while feed forward back propagation neural network (FFBP-NN), generalized regression neural network (GR-NN) and radial basis function neural network (RBF-NN) are used to represent the non-parametric approach. Monthly water level data in Lake Urmia observed for 1966–2010 are used for the case study. Four criteria, coefficient of determination, Lin’s concordance correlation coefficient, performance index and root mean square percentage error are used in comparison of the models. The first two are considered for the success of the models while the last two for the failure. Success criteria are given a grade between 0 and 10, failure criteria receive a grade from −10 to 0. The summation of the grades is taken as the total grade of each model. It is found that regression models and FFBP-NN are superior to GR-NN, RBF-NN and DT in predicting monthly lake water level.

Suggested Citation

  • Babak Vaheddoost & Hafzullah Aksoy & Hirad Abghari, 2016. "Prediction of Water Level using Monthly Lagged Data in Lake Urmia, Iran," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4951-4967, October.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:13:d:10.1007_s11269-016-1463-y
    DOI: 10.1007/s11269-016-1463-y
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    References listed on IDEAS

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    1. Hossein Kakahaji & Hamed Banadaki & Abbas Kakahaji & Abdulamir Kakahaji, 2013. "Prediction of Urmia Lake Water-Level Fluctuations by Using Analytical, Linear Statistic and Intelligent Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(13), pages 4469-4492, October.
    2. Elmira Hassanzadeh & Mahdi Zarghami & Yousef Hassanzadeh, 2012. "Determining the Main Factors in Declining the Urmia Lake Level by Using System Dynamics Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(1), pages 129-145, January.
    3. Kisi, Ozgur & Shiri, Jalal & Karimi, Sepideh & Shamshirband, Shahaboddin & Motamedi, Shervin & Petković, Dalibor & Hashim, Roslan, 2015. "A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 731-743.
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    Cited by:

    1. Babak Vaheddoost & Hafzullah Aksoy, 2019. "Reconstruction of Hydrometeorological Data in Lake Urmia Basin by Frequency Domain Analysis Using Additive Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3899-3911, September.
    2. Hossein Bonakdari & Isa Ebtehaj & Pijush Samui & Bahram Gharabaghi, 2019. "Lake Water-Level fluctuations forecasting using Minimax Probability Machine Regression, Relevance Vector Machine, Gaussian Process Regression, and Extreme Learning Machine," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3965-3984, September.

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