An optimal integration of multiple machine learning techniques to real-time reservoir inflow forecasting

Abstract A reservoir inflow forecasting system represents a crucial technique in reservoir operation and disaster prevention, particularly in areas where the primary water source derives from typhoon events. This includes the study area of the current research, i.e., the Shihmen Reservoir (Taiwan)....
Ausführliche Beschreibung

Abstract A reservoir inflow forecasting system represents a crucial technique in reservoir operation and disaster prevention, particularly in areas where the primary water source derives from typhoon events. This includes the study area of the current research, i.e., the Shihmen Reservoir (Taiwan). Effectively depositing short and high-intensity rainfall and avoiding disaster losses present significant challenges in this regard. However, the high variability and uncertainty of such rainfall events make them difficult to forecast using traditional physical-based models, which require too many calculations for application in real-time disaster forecasting. Accordingly, in this study, seven machine learning (ML) algorithms, including three conventional ML and four deep learning algorithms, were compared to derive their effectiveness for reservoir inflow forecasting in extreme weather events. The forecasting lead-times were set to 1, 4, and 6-h, representing short, medium, and long-term forecasting, respectively. Moreover, to ensure the stability and credibility of the models, two types of integrated approaches, ensemble means and switched prediction method (SP) were also employed. The results showed that although an optimal algorithm could be selected for the short, medium, and long-term, individual algorithms did not always perform well in all events. Nonetheless, the integrated approaches can effectively combine the advantages of all the included algorithms and generate more accurate and stable forecasting results, particularly when using SP, which was involved in the top three performances among all typhoon examples and indicated the best average performance. In the short-term forecast, the RMSE of the testing events is 107.2 $ m^{3} $/s while using SP, ranking 3rd among all 9 methods. In the medium-term forecast, the RMSE predicted by the SP is 281.72 $ m^{3} $/s (Rank = 1). In the long-term forecast, the SP also performed the best among the 9 methods, and the RMSE was 477.14 $ m^{3} $/s. In conclusion, if only single model forecast is considered, gated recurrent unit, a type of transformed recurrent neural network, will yield the best performance. Furthermore, integrated forecasts, particularly involving SP, can effectively improve the accuracy and stability of forecasts to render a model more applicable to an actual situation. Ausführliche Beschreibung

Autor*in:	Huang, I.-Hang [verfasserIn] Chang, Ming-Jui Lin, Gwo-Fong

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Reservoir inflow forecasting Ensemble forecasting Machine learning Deep learning Switched prediction

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: Stochastic environmental research and risk assessment - Berlin : Springer, 1987, 36(2021), 6 vom: 04. Sept., Seite 1541-1561
Übergeordnetes Werk:	volume:36 ; year:2021 ; number:6 ; day:04 ; month:09 ; pages:1541-1561

Links:	Volltext

DOI / URN:	10.1007/s00477-021-02085-y

Katalog-ID:	SPR047033193