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A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm

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  • Kisi, Ozgur
  • Shiri, Jalal
  • Karimi, Sepideh
  • Shamshirband, Shahaboddin
  • Motamedi, Shervin
  • Petković, Dalibor
  • Hashim, Roslan

Abstract

Forecasting lake level at various horizons is a critical issue in navigation, water resource planning and catchment management. In this article, multistep ahead predictive models of predicting daily lake levels for three prediction horizons were created. The models were developed using a novel method based on support vector machine (SVM) coupled with firefly algorithm (FA). The FA was applied to estimate the optimal SVM parameters. Daily water-level data from Urmia Lake in northwestern Iran were used to train, test and validate the used technique. The prediction results of the SVM–FA models were compared to the genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results showed that an improvement in the predictive accuracy and capability of generalization can be achieved by the SVM–FA approach in comparison to the GP and ANN in 1 day ahead lake level forecast. Moreover, the findings indicated that the developed SVM–FA models can be used with confidence for further work on formulating a novel model of predictive strategy for lake level prediction.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:apmaco:v:270:y:2015:i:c:p:731-743
    DOI: 10.1016/j.amc.2015.08.085
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    1. 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.
    2. Seyed Ahmad Soleymani & Shidrokh Goudarzi & Mohammad Hossein Anisi & Wan Haslina Hassan & Mohd Yamani Idna Idris & Shahaboddin Shamshirband & Noorzaily Mohamed Noor & Ismail Ahmedy, 2016. "A Novel Method to Water Level Prediction using RBF and FFA," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 3265-3283, July.
    3. 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.
    4. Zeng, Tao & Zhang, Caizhi & Hu, Minghui & Chen, Yan & Yuan, Changrong & Chen, Jingrui & Zhou, Anjian, 2018. "Modelling and predicting energy consumption of a range extender fuel cell hybrid vehicle," Energy, Elsevier, vol. 165(PB), pages 187-197.
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    7. Zeng, Tao & Zhang, Caizhi & Zhou, Anjian & Wu, Qi & Deng, Chenghao & Chan, Siew Hwa & Chen, Jinrui & Foley, Aoife M., 2021. "Enhancing reactant mass transfer inside fuel cells to improve dynamic performance via intelligent hydrogen pressure control," Energy, Elsevier, vol. 230(C).
    8. M. Zulfiqar & Kelum A. A. Gamage & M. B. Rasheed & C. Gould, 2024. "Optimised Deep Learning for Time-Critical Load Forecasting Using LSTM and Modified Particle Swarm Optimisation," Energies, MDPI, vol. 17(22), pages 1-27, November.
    9. 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.
    10. 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.
    11. Shaghaghi, Saba & Bonakdari, Hossein & Gholami, Azadeh & Ebtehaj, Isa & Zeinolabedini, Maryam, 2017. "Comparative analysis of GMDH neural network based on genetic algorithm and particle swarm optimization in stable channel design," Applied Mathematics and Computation, Elsevier, vol. 313(C), pages 271-286.
    12. Siddik Shakul Hameed & Ramesh Ramadoss & Kannadasan Raju & GM Shafiullah, 2022. "A Framework-Based Wind Forecasting to Assess Wind Potential with Improved Grey Wolf Optimization and Support Vector Regression," Sustainability, MDPI, vol. 14(7), pages 1-29, April.
    13. Sri Lakshmi Sesha Vani Jayanthi & Venkata Reddy Keesara & Venkataramana Sridhar, 2022. "Prediction of Future Lake Water Availability Using SWAT and Support Vector Regression (SVR)," Sustainability, MDPI, vol. 14(12), pages 1-17, June.
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