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Monthly River Discharge Forecasting Using Hybrid Models Based on Extreme Gradient Boosting Coupled with Wavelet Theory and Lévy–Jaya Optimization Algorithm

Author

Listed:
  • Jincheng Zhou

    (Qiannan Normal University for Nationalities
    Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province
    Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan)

  • Dan Wang

    (Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province
    Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan
    Qiannan Normal University for Nationalities)

  • Shahab S. Band

    (National Yunlin University of Science and Technology
    National Yunlin University of Science and Technology)

  • Changhyun Jun

    (Chung-Ang University)

  • Sayed M. Bateni

    (University of Hawaii at Manoa)

  • M. Moslehpour

    (Asia University)

  • Hao-Ting Pai

    (National Pingtung University)

  • Chung-Chian Hsu

    (National Yunlin University of Science and Technology)

  • Rasoul Ameri

    (National Yunlin University of Science and Technology)

Abstract

River discharge represents critical hydrological data that can be used to monitor the hydrological status of a river basin. The objective of this study was to forecast the monthly river discharge time-series of two gauging hydrometric sites (USGS 06054500 and USGS 06090800) located on the Missouri River, USA. The forecast was performed using two machine learning models based on extreme gradient boosting (XGB) and K-nearest neighbors (KNN). XGB outperformed the KNN framework in forecasting the river flow. Subsequently, wavelet (W) analysis was incorporated to develop the hybrid W-XGB and W-KNN approaches. Finally, two novel hybrid models were established through the hybridization of XGB and the Lévy–Jaya optimization algorithm (LJA) and simultaneous integration of the wavelet analysis and LJA with the XGB, i.e., XGB-LJA and W-XGB-LJA, respectively. The performances of the models were evaluated using the root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE), determination coefficient (R ), and Nash–Sutcliffe efficiency (NSE). In the test phase, the best discharge forecasts at USGS 06054500 and USGS 06090800 were obtained using the hybrid WXGB2-LJA (RMSE = 41.303 m /s, MAE = 28.752 m /s, MBE = 3.377 m /s, R = 0.819, NSE = 0.800) and W-XGB4-LJA (RMSE = 39.310 m /s, MAE = 26.804 m /s, MBE = 1.489 m3/s, R = 0.897, NSE = 0.885), respectively.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:10:d:10.1007_s11269-023-03534-9
    DOI: 10.1007/s11269-023-03534-9
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    1. Khabat Khosravi & Ali Golkarian & John P. Tiefenbacher, 2022. "Using Optimized Deep Learning to Predict Daily Streamflow: A Comparison to Common Machine Learning Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 699-716, January.
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    Cited by:

    1. Xi Yang & Zhihe Chen & Min Qin, 2024. "Monthly Runoff Prediction Via Mode Decomposition-Recombination Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 269-286, January.

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