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Improved river water-stage forecasts by ensemble learning

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Abstract

Forecasting water stages is of significance to river and reservoir management. However, conventional models sometimes fail to perform accurately, as water levels are characterized by high nonstationarity. To provide an improved estimation of water stages, this study develops a new prediction framework by coupling stand-alone machine learning models with ensemble algorithms. As base learners, the optimal regression tree (RT) and extreme learning machine (ELM) are integrated into four ensemble strategies, i.e., bagging (BA), boosting (BO), random forest (RF) and random subspace (RS), leading to eight ensemble models. They are then assessed using daily water-stage records at two hydrological stations on the Yangtze River. Their performance is evaluated by statistical criteria: coefficient of determination (CD), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE) and mean absolute error (MAE). The RT and the ELM generate satisfactory predictions with deficiency in capturing extreme values. The ensemble models generally enhance the prediction efficiency, with their mean CD and NSE augment by up to 6.9% and 7.0%, and mean RMSE and MAE reduction by up to 47.9% and 47.0%. The BO-based models, namely BO-RT and BO-ELM, result in the highest accuracy, with a mean absolute relative error (ARE) of 1.0% and 1.4%. Ensemble learning gains even in multi-step-ahead forecasts, which satisfactorily extends the lead time up to 14 days. This study illustrates the capability of ensemble learning for improved water-level forecasts, which provides reference for modeling related issues such as sediment load and rainfall-runoff.

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Acknowledgements

The study is funded by the Swedish Hydropower Center (SVC). It is part of research projects “Two-phase flow modeling: evaluations and simulations for safer spillway discharge” and “Quality and trust of numerical modeling of water-air flows for safe spillway discharge”, with James Yang and Anders Ansell as project leaders. SVC has been established by the Swedish Energy Agency, Energiforsk and Svenska Kraftnät, together with Royal Institute of Technology (KTH), Luleå University of Technology (LTU), Uppsala University (UU) and Chalmers University of Technology (CTH). Participating companies and industry associations include AFRY, Andritz Hydro, Boliden, Fortum Generation, Holmen Energi, Jämtkraft, Karlstads Energi, LKAB, Mälarenergi, Norconsult, Rainpower, Skellefteå Kraft, Sollefteåforsens, Statkraft Sverige, Sweco Energuide, Sweco Infrastructure, Tekniska verken i Linköping, Uniper, Vattenfall R&D, Vattenfall Vattenkraft, Voith Hydro, WSP Sverige and Zinkgruvan. The assistance from Holger Ecke of Vattenfall R&D and Qiancheng Xie of LTU is acknowledged.

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  1. Department of Civil and Architectural Engineering, KTH Royal Institute of Technology, Stockholm, Sweden

    Shicheng Li & James Yang

  2. R&D Hydraulic Laboratory, Vattenfall AB, Älvkarleby, Sweden

    James Yang

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Li, S., Yang, J. Improved river water-stage forecasts by ensemble learning. Engineering with Computers 39, 3293–3311 (2023). https://doi.org/10.1007/s00366-022-01751-1

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