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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

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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.

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Data Availability

The public data used in this study is available at https://waterdata.usgs.gov/nwis.

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Funding

This research was funded by Key the National Natural Science Foundation of China under Grant No.61862051; the Science and Technology Foundation of Guizhou Province under Grant No.ZK[2022]549; the Natural Science Foundation of Education of Guizhou province under Grant No.s([2019]203, KY[2019]067); the program of Qiannan Normal University for Nationalities under Grant Nos. (qnsy2018003, qnsy2019rc09).

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Authors and Affiliations

  1. School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou, 558000, China

    Jincheng Zhou

  2. Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province, Duyun, 558000, China

    Jincheng Zhou & Dan Wang

  3. Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan, Duyun, 558000, China

    Jincheng Zhou & Dan Wang

  4. School of Mathematics and Statistics, Qiannan Normal University for Nationalities, Duyun, Guizhou, 558000, People’s Republic of China

    Dan Wang

  5. Department of Information Management, International Graduate Institute of Artificial Intelligence, National Yunlin University of Science and Technology, Douliou, Taiwan

    Shahab S. Band

  6. Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Taiwan

    Shahab S. Band

  7. Department of Civil and Environmental Engineering, College of Engineering, Chung-Ang University, Seoul, Korea

    Changhyun Jun

  8. Department of Civil and Environmental Engineering, and Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI, USA

    Sayed M. Bateni

  9. Department of Business Administration, College of Management, Asia University, Taichung, 41354, Taiwan

    M. Moslehpour

  10. Bachelor Program of Big Data Applications in Business, National Pingtung University, Pingtung, Taiwan

    Hao-Ting Pai

  11. Department of Information Management, International Graduate Institute of Artificial Intelligence, National Yunlin University of Science and Technology, Douliou, Taiwan

    Chung-Chian Hsu

  12. Department of Information Management, National Yunlin University of Science and Technology, Douliou, Taiwan

    Rasoul Ameri

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  1. Jincheng Zhou
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Zhou, J., Wang, D., Band, S.S. et al. Monthly River Discharge Forecasting Using Hybrid Models Based on Extreme Gradient Boosting Coupled with Wavelet Theory and Lévy–Jaya Optimization Algorithm. Water Resour Manage 37, 3953–3972 (2023). https://doi.org/10.1007/s11269-023-03534-9

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