Assessment of Different Machine Learning Methods for Reservoir Outflow Forecasting

Reservoirs play an important function in human society due to their ability to hold and regulate the flow. This will play a key role in the future decades due to climate change. Therefore, having reliable predictions of the outflow from a reservoir is necessary for early warning systems and adequate...

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Bibliographic Details
Published in:Water (Basel) 2023-10, Vol.15 (19), p.3380
Main Authors: Soria-Lopez, Anton, Sobrido-Pouso, Carlos, Mejuto, Juan C., Astray, Gonzalo
Format: Article
Language:English
Subjects:
Agricultural production
Algorithms
Climate change
Dams
Decision making
Floods
Forecasts and trends
Hydraulic measurements
Hydroelectric power
Hydrology
Machine learning
Methods
Neural networks
Spain
Storm damage
Time series
Variables
Water supply
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Summary:Reservoirs play an important function in human society due to their ability to hold and regulate the flow. This will play a key role in the future decades due to climate change. Therefore, having reliable predictions of the outflow from a reservoir is necessary for early warning systems and adequate water management. In this sense, this study uses three approaches machine learning (ML)-based techniques—Random Forest (RF), Support Vector Machine (SVM) and artificial neural network (ANN)—to predict outflow one day ahead of eight different dams belonging to the Miño-Sil Hydrographic Confederation (Galicia, Spain), using three input variables of the current day. Mostly, the results obtained showed that the suggested models work correctly in predicting reservoir outflow in normal conditions. Among the different ML approaches analyzed, ANN was the most appropriate technique since it was the one that provided the best model in five reservoirs.
ISSN:2073-4441
2073-4441
DOI:10.3390/w15193380