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

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

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...

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Bibliographic Details
Main Authors: C. Castillo-Botón, D. Casillas-Pérez, C. Casanova-Mateo, L. M. Moreno-Saavedra, B. Morales-Díaz, J. Sanz-Justo, P. A. Gutiérrez and S. Salcedo-Sanz
Format: Article
Language:English
Published: MDPI AG 2020-05-01
Series:Water
Subjects:
dammed water level
hydropower reservoirs
detrended fluctuation analysis
ARMA models
machine learning regressors
reservoir management
Online Access:https://www.mdpi.com/2073-4441/12/6/1528

Internet

https://www.mdpi.com/2073-4441/12/6/1528

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