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Advancing Freshwater Lake Level Forecast Using King’s Castle Optimization with Training Sample Adaption and Adaptive Neuro-Fuzzy Inference System

Author

Listed:
  • Amir Hossein Zaji

    (Razi University)

  • Hossein Bonakdari

    (Razi University
    University of Guelph)

  • Bahram Gharabaghi

    (University of Guelph)

Abstract

This study presents a novel method for more accurate forecasting freshwater Lake Levels with complex fluctuation patterns due to multiple anthropogenic demands and climate factors. The new method employs the mighty King’s Castle Optimization (KCO) with Training Sample Adaption (TSA) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to develop a novel hybrid KCO-TSA-ANFIS model. The performance of the new KCO-TSA-ANFIS Lake water level forecast model is tested on the monthly water levels of Lake Van, in Turkey, showing significantly improved accuracy in model forecasts compared with the regular ANFIS model. By comparing the Root Mean Square Error (RMSE) results, it can be concluded that the KCO-TSA-ANFIS method has 71% higher performance than the simple ANFIS method.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:12:d:10.1007_s11269-019-02356-y
    DOI: 10.1007/s11269-019-02356-y
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    References listed on IDEAS

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    1. Veysel Güldal & Hakan Tongal, 2010. "Comparison of Recurrent Neural Network, Adaptive Neuro-Fuzzy Inference System and Stochastic Models in Eğirdir Lake Level Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(1), pages 105-128, January.
    2. 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.
    3. Maryam Shafaei & Ozgur Kisi, 2016. "Lake Level Forecasting Using Wavelet-SVR, Wavelet-ANFIS and Wavelet-ARMA Conjunction Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 79-97, January.
    4. Hamid Moeeni & Hossein Bonakdari & Isa Ebtehaj, 2017. "Integrated SARIMA with Neuro-Fuzzy Systems and Neural Networks for Monthly Inflow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(7), pages 2141-2156, May.
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