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Prediction of Water-Level in the Urmia Lake Using the Extreme Learning Machine Approach

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Abstract

Predicting the dynamics of water-level in lakes plays a vital role in navigation, water resources planning and catchment management. In this paper, the Extreme Learning Machine (ELM) approach was used to predict the daily water-level in the Urmia Lake. Daily water-level data from the Urmia Lake in northwest of Iran were used to train, test and validate the employed models. Results showed that the ELM approach can accurately forecast the water-level in the Urmia Lake. Outcomes from the ELM model were also compared with those of genetic programming (GP) and artificial neural networks (ANNs). It was found that the ELM technique outperforms GP and ANN in predicting water-level in the Urmia Lake. It also can learn the relation between the water-level and its influential variables much faster than the GP and ANN. Overall, the results show that the ELM approach can be used to predict dynamics of water-level in lakes.

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Authors and Affiliations

  1. Water Engineering Department, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

    Jalal Shiri & Sepideh Karimi

  2. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Shahaboddin Shamshirband

  3. Civil Engineering Department, Architectural and Engineering Faculty, Canik Basari University, Samsun, Turkey

    Ozgur Kisi

  4. Department of Civil and Environmental Engineering & Water Resources Research Center, University of Hawaii, Manoa, HI, USA

    Seyyed M Bateni

  5. Department of Computer Engineering, Ahar Branch, Islamic Azad University, Ahar, Iran

    Seyed Hossein Hosseini Nezhad

  6. Department of Civil Engineering, University of Tabriz, Tabriz, Iran

    Arsalan Hashemi

Authors
  1. Jalal Shiri
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  2. Shahaboddin Shamshirband
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  3. Ozgur Kisi
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  4. Sepideh Karimi
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  5. Seyyed M Bateni
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  6. Seyed Hossein Hosseini Nezhad
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  7. Arsalan Hashemi
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Corresponding authors

Correspondence to Jalal Shiri or Shahaboddin Shamshirband.

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Shiri, J., Shamshirband, S., Kisi, O. et al. Prediction of Water-Level in the Urmia Lake Using the Extreme Learning Machine Approach. Water Resour Manage 30, 5217–5229 (2016). https://doi.org/10.1007/s11269-016-1480-x

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  • DOI: https://doi.org/10.1007/s11269-016-1480-x

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