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Daily Scale River Flow Forecasting Using Hybrid Gradient Boosting Model with Genetic Algorithm Optimization

Author

Listed:
  • Huseyin Cagan Kilinc

    (İstanbul Aydın University)

  • Iman Ahmadianfar

    (Behbahan Khatam Alanbia University of Technology)

  • Vahdettin Demir

    (KTO Karatay University)

  • Salim Heddam

    (University 20 Août 1955 Skikda)

  • Ahmed M. Al-Areeq

    (King Fahd University of Petroleum & Minerals (KFUPM))

  • Sani I. Abba

    (King Fahd University of Petroleum & Minerals (KFUPM))

  • Mou Leong Tan

    (Universiti Sains Malaysia
    Nanjing Normal University)

  • Bijay Halder

    (Vidyasagar University)

  • Haydar Abdulameer Marhoon

    (Al-Ayen University
    University of Kerbala)

  • Zaher Mundher Yaseen

    (King Fahd University of Petroleum & Minerals (KFUPM)
    King Fahd University of Petroleum & Minerals)

Abstract

Accurate and sustainable management of water resources is among the most important circumstances of basin and river engineering. In this study, a hybrid machine learning (ML) model was generated using CatBoost and Genetic Algorithm (GA) for significant impact on river flow prediction. The study was applied to Sakarya Basin, which is located in semi-arid climatic conditions in Turkey. The forecast performance of the models was observed by developing a day-step ahead forecast scenario with the data of Adatepe, Aktaş and Rüstümköy flow measurement stations (FMS). The daily flow data of the specified stations between 2002 and 2012 were used and the performance of the proposed model was tested by comparing with CatBoost, Long-Short Term Memory (LSTM) and the classical estimation method, Linear Regression (LR). The study was also aimed to improve the predictive performance of genetic algorithms combined with the gradient boosting model (GA-CatBoost). The developed hybrid model outperformed the benchmarked models. The results showed that the developed model can be successfully applied in river flow forecasting.

Suggested Citation

  • Huseyin Cagan Kilinc & Iman Ahmadianfar & Vahdettin Demir & Salim Heddam & Ahmed M. Al-Areeq & Sani I. Abba & Mou Leong Tan & Bijay Halder & Haydar Abdulameer Marhoon & Zaher Mundher Yaseen, 2023. "Daily Scale River Flow Forecasting Using Hybrid Gradient Boosting Model with Genetic Algorithm Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3699-3714, July.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:9:d:10.1007_s11269-023-03522-z
    DOI: 10.1007/s11269-023-03522-z
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    References listed on IDEAS

    as
    1. Lili Wang & Yanlong Guo & Manhong Fan, 2022. "Improving Annual Streamflow Prediction by Extracting Information from High-frequency Components of Streamflow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4535-4555, September.
    2. Yukseltan, Ergun & Yucekaya, Ahmet & Bilge, Ayse Humeyra & Agca Aktunc, Esra, 2021. "Forecasting models for daily natural gas consumption considering periodic variations and demand segregation," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    3. Gokmen Ceribasi & Ahmet Iyad Ceyhunlu & Andrzej Wałęga & Dariusz Młyński, 2022. "Investigation of the Effect of Climate Change on Energy Produced by Hydroelectric Power Plants (HEPPs) by Trend Analysis Method: A Case Study for Dogancay I–II HEPPs," Energies, MDPI, vol. 15(7), pages 1-17, March.
    4. Sanjay Kumar Singh & Anjali Goyal, 2020. "Performance Analysis of Machine Learning Algorithms for Cervical Cancer Detection," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 15(2), pages 1-21, April.
    5. Xinxin He & Jungang Luo & Peng Li & Ganggang Zuo & Jiancang Xie, 2020. "A Hybrid Model Based on Variational Mode Decomposition and Gradient Boosting Regression Tree for Monthly Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 865-884, January.
    6. Taís Maria Nunes Carvalho & Francisco Souza Filho, 2021. "Variational Mode Decomposition Hybridized With Gradient Boost Regression for Seasonal Forecast of Residential Water Demand," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(10), pages 3431-3445, August.
    7. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Yan, Yichuan & Tian, Ning, 2023. "Prediction of fluctuation loads based on GARCH family-CatBoost-CNNLSTM," Energy, Elsevier, vol. 263(PE).
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