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Article

Analysis and Prediction of Dammed Water Level in a Hydropower Reservoir Using Machine Learning and Persistence-Based Techniques

1
Department of Signal Processing and Communications, Universidad de Alcala, 28805 Alcalá de Henares, Spain
2
LATUV, Remote Sensing Laboratory, Universidad de Valladolid, 47011 Valladolid, Spain
3
Department of Computer Science and Numerical Analysis, Universidad de Córdoba, 14014 Córdoba, Spain
*
Author to whom correspondence should be addressed.
Water 2020, 12(6), 1528; https://doi.org/10.3390/w12061528
Received: 6 April 2020 / Revised: 20 May 2020 / Accepted: 21 May 2020 / Published: 27 May 2020
(This article belongs to the Special Issue Water Resources Management: Advances in Machine Learning Approaches)
This paper presents long- and short-term analyses and predictions of dammed water level in a hydropower reservoir. The long-term analysis was carried out by using techniques such as detrended fluctuation analysis, auto-regressive models, and persistence-based algorithms. On the other hand, the short-term analysis of the dammed water level in the hydropower reservoir was modeled as a prediction problem, where machine learning regression techniques were studied. A set of models, including different types of neural networks, Support Vector regression, or Gaussian processes was tested. Real data from a hydropower reservoir located in Galicia, Spain, qwew considered, together with predictive variables from upstream measuring stations. We show that the techniques presented in this paper offer an excellent tool for the long- and short-term analysis and prediction of dammed water level in reservoirs for hydropower purposes, especially important for the management of water resources in areas with hydrology stress, such as Spain. View Full-Text
Keywords: dammed water level; hydropower reservoirs; detrended fluctuation analysis; ARMA models; machine learning regressors; reservoir management dammed water level; hydropower reservoirs; detrended fluctuation analysis; ARMA models; machine learning regressors; reservoir management
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MDPI and ACS Style

Castillo-Botón, C.; Casillas-Pérez, D.; Casanova-Mateo, C.; Moreno-Saavedra, L.M.; Morales-Díaz, B.; Sanz-Justo, J.; Gutiérrez, P.A.; Salcedo-Sanz, S. Analysis and Prediction of Dammed Water Level in a Hydropower Reservoir Using Machine Learning and Persistence-Based Techniques. Water 2020, 12, 1528. https://doi.org/10.3390/w12061528

AMA Style

Castillo-Botón C, Casillas-Pérez D, Casanova-Mateo C, Moreno-Saavedra LM, Morales-Díaz B, Sanz-Justo J, Gutiérrez PA, Salcedo-Sanz S. Analysis and Prediction of Dammed Water Level in a Hydropower Reservoir Using Machine Learning and Persistence-Based Techniques. Water. 2020; 12(6):1528. https://doi.org/10.3390/w12061528

Chicago/Turabian Style

Castillo-Botón, C., D. Casillas-Pérez, C. Casanova-Mateo, L. M. Moreno-Saavedra, B. Morales-Díaz, J. Sanz-Justo, P. A. Gutiérrez, and S. Salcedo-Sanz 2020. "Analysis and Prediction of Dammed Water Level in a Hydropower Reservoir Using Machine Learning and Persistence-Based Techniques" Water 12, no. 6: 1528. https://doi.org/10.3390/w12061528

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