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

Author

Listed:
  • Jalal Shiri

    (University of Tabriz)

  • Shahaboddin Shamshirband

    (University of Malaya)

  • Ozgur Kisi

    (Canik Basari University)

  • Sepideh Karimi

    (University of Tabriz)

  • Seyyed M Bateni

    (University of Hawaii)

  • Seyed Hossein Hosseini Nezhad

    (Islamic Azad University)

  • Arsalan Hashemi

    (University of Tabriz)

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.

Suggested Citation

  • Jalal Shiri & Shahaboddin Shamshirband & Ozgur Kisi & Sepideh Karimi & Seyyed M Bateni & Seyed Hossein Hosseini Nezhad & Arsalan Hashemi, 2016. "Prediction of Water-Level in the Urmia Lake Using the Extreme Learning Machine Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5217-5229, November.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:14:d:10.1007_s11269-016-1480-x
    DOI: 10.1007/s11269-016-1480-x
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    References listed on IDEAS

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    1. Meral Buyukyildiz & Gulay Tezel & Volkan Yilmaz, 2014. "Estimation of the Change in Lake Water Level by Artificial Intelligence Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4747-4763, October.
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    5. 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.
    6. Muhammad Sulaiman & Ahmed El-Shafie & Othman Karim & Hassan Basri, 2011. "Improved Water Level Forecasting Performance by Using Optimal Steepness Coefficients in an Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(10), pages 2525-2541, August.
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    Cited by:

    1. Amir Hossein Zaji & Hossein Bonakdari & Bahram Gharabaghi, 2019. "Advancing Freshwater Lake Level Forecast Using King’s Castle Optimization with Training Sample Adaption and Adaptive Neuro-Fuzzy Inference System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(12), pages 4215-4230, September.
    2. Yawei Qin & Yongjin Lei & Xiangyu Gong & Wanglai Ju, 2022. "A model involving meteorological factors for short- to medium-term, water-level predictions of small- and medium-sized urban rivers," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(1), pages 725-739, March.
    3. Balati Maihemuti & Tayierjiang Aishan & Zibibula Simayi & Yilinuer Alifujiang & Shengtian Yang, 2020. "Temporal Scaling of Water Level Fluctuations in Shallow Lakes and Its Impacts on the Lake Eco-Environments," Sustainability, MDPI, vol. 12(9), pages 1-14, April.
    4. 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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