IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v37y2023i9d10.1007_s11269-023-03522-z.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-023-03522-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-023-03522-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tong, Jianfeng & Liu, Zhenxing & Zhang, Yong & Zheng, Xiujuan & Jin, Junyang, 2023. "Improved multi-gate mixture-of-experts framework for multi-step prediction of gas load," Energy, Elsevier, vol. 282(C).
    2. 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.
    3. Sarmad Dashti Latif & Ali Najah Ahmed, 2023. "A review of deep learning and machine learning techniques for hydrological inflow forecasting," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 12189-12216, November.
    4. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Yan, Yichuan & Tian, Ning, 2023. "Natural gas demand response strategy considering user satisfaction and load volatility under dynamic pricing," Energy, Elsevier, vol. 277(C).
    5. Yu, Ruyang & Zhang, Kai & Ramasubramanian, Brindha & Jiang, Shu & Ramakrishna, Seeram & Tang, Yuhang, 2024. "Ensemble learning for predicting average thermal extraction load of a hydrothermal geothermal field: A case study in Guanzhong Basin, China," Energy, Elsevier, vol. 296(C).
    6. Wang, Yingnan & Chen, Xu & Zhao, Chunhui, 2024. "A data-driven soft sensor model for coal-fired boiler SO2 concentration prediction with non-stationary characteristic," Energy, Elsevier, vol. 300(C).
    7. Xu, Yifan & Che, Jinxing & Xia, Wenxin & Hu, Kun & Jiang, Weirui, 2024. "A novel paradigm: Addressing real-time decomposition challenges in carbon price prediction," Applied Energy, Elsevier, vol. 364(C).
    8. Lin, Zijie & Xie, Linbo & Zhang, Siyuan, 2024. "A compound framework for short-term gas load forecasting combining time-enhanced perception transformer and two-stage feature extraction," Energy, Elsevier, vol. 298(C).
    9. Jincheng Zhou & Dan Wang & Shahab S. Band & Changhyun Jun & Sayed M. Bateni & M. Moslehpour & Hao-Ting Pai & Chung-Chian Hsu & Rasoul Ameri, 2023. "Monthly River Discharge Forecasting Using Hybrid Models Based on Extreme Gradient Boosting Coupled with Wavelet Theory and Lévy–Jaya Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 3953-3972, August.
    10. Kang, Yan & Chen, Peiru & Cheng, Xiao & Zhang, Shuo & Song, Songbai, 2022. "Novel hybrid machine learning framework with decomposition–transformation and identification of key modes for estimating reference evapotranspiration," Agricultural Water Management, Elsevier, vol. 273(C).
    11. Ding, Lili & Zhao, Zhongchao & Wang, Lei, 2022. "Probability density forecasts for natural gas demand in China: Do mixed-frequency dynamic factors matter?," Applied Energy, Elsevier, vol. 312(C).
    12. Wang, Jujie & Cui, Quan & He, Maolin, 2022. "Hybrid intelligent framework for carbon price prediction using improved variational mode decomposition and optimal extreme learning machine," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    13. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Tian, Ning & Zhao, Wei, 2023. "Incentive-based demand response strategies for natural gas considering carbon emissions and load volatility," Applied Energy, Elsevier, vol. 348(C).
    14. Zhennan Liu & Qiongfang Li & Jingnan Zhou & Weiguo Jiao & Xiaoyu Wang, 2021. "Runoff Prediction Using a Novel Hybrid ANFIS Model Based on Variable Screening," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2921-2940, July.
    15. Min Gan & Xijun Lai & Yan Guo & Yongping Chen & Shunqi Pan & Yinghao Zhang, 2024. "Floodplain Lake Water Level Prediction with Strong River-Lake Interaction Using the Ensemble Learning LightGBM," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(13), pages 5305-5321, October.
    16. Wen-chuan Wang & Yu-jin Du & Kwok-wing Chau & Dong-mei Xu & Chang-jun Liu & Qiang Ma, 2021. "An Ensemble Hybrid Forecasting Model for Annual Runoff Based on Sample Entropy, Secondary Decomposition, and Long Short-Term Memory Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(14), pages 4695-4726, November.
    17. Hui Hu & Jianfeng Zhang & Tao Li, 2021. "A Novel Hybrid Decompose-Ensemble Strategy with a VMD-BPNN Approach for Daily Streamflow Estimating," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5119-5138, December.
    18. Yani Lian & Jungang Luo & Wei Xue & Ganggang Zuo & Shangyao Zhang, 2022. "Cause-driven Streamflow Forecasting Framework Based on Linear Correlation Reconstruction and Long Short-term Memory," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(5), pages 1661-1678, March.
    19. Hexiang Zheng & Hongfei Hou & Ziyuan Qin, 2024. "Research on a Non-Stationary Groundwater Level Prediction Model Based on VMD-iTransformer and Its Application in Sustainable Water Resource Management of Ecological Reserves," Sustainability, MDPI, vol. 16(21), pages 1-18, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:37:y:2023:i:9:d:10.1007_s11269-023-03522-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.