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

Water

卷:12

P.A. Gutiérrez and S. Salcedo-Sanz¹  L.M. Moreno-Saavedra²  C. Castillo-Botón²  D. Casillas-Pérez²  B. Morales-Díaz²  J. Sanz-Justo³  C. Casanova-Mateo³ 

[1] Department of Computer Science and Numerical Analysis, Universidad de Córdoba, Córdoba 14014, Spain;

[2] Department of Signal Processing and Communications, Universidad de Alcala, Alcalá de Henares 28805, Spain;

[3] LATUV, Remote Sensing Laboratory, Universidad de Valladolid, Valladolid 47011, Spain;

关键词: dammed water level;    hydropower reservoirs;    detrended fluctuation analysis;    ARMA models;    machine learning regressors;    reservoir management;   

DOI  :  10.3390/w12061528

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown