Preprints
https://doi.org/10.5194/nhess-2022-171
https://doi.org/10.5194/nhess-2022-171
 
17 Jun 2022
17 Jun 2022
Status: this preprint is currently under review for the journal NHESS.

Comparison of machine learning techniques for reservoir outflow forecasting

Orlando García-Feal1,2, José González-Cao1, Diego Fernández-Nóvoa1, Gonzalo Astray Dopazo3, and Moncho Gómez-Gesteira1 Orlando García-Feal et al.
  • 1Centro de Investigación Mariña, Universidade de Vigo, Environmental Physics Laboratory (EPhysLab), Campus Auga, Ourense, 32004, España
  • 2Water and Environmental Engineering Group, Department of Civil Engineering, Universidade da Coruña, A Coruña, 15071, España
  • 3Universidade de Vigo, Departamento de Química Física, Facultade de Ciencias, 32004 Ourense, España

Abstract. Reservoirs play a key role in many human societies due to their capability to manage water resources. In addition to their role in water supply and hydropower production, their ability to retain water and control the flow makes them a valuable asset for flood mitigation. This is a key function since extreme events have increased in the last decades as a result of climate change, and therefore, the application of mechanisms capable of mitigating flood damage will be key in the coming decades. Having a good estimation of the outflow of a reservoir can be an advantage for water management or early warning systems. When historical data are available, data-driven models have been proven a useful tool for different hydrological applications. In this sense, this study analyses the efficiency of different machine learning techniques to predict reservoir outflow, namely multivariate linear regression (MLR) and three artificial neural networks: multilayer perceptron (MLP), nonlinear autoregressive exogenous (NARX) and long short-term memory (LSTM). These techniques were applied to forecast the outflow of eight water reservoirs of different characteristics located in the Miño River (northwest of Spain). In general, the results obtained showed that the proposed models provided a good estimation of the outflow of the reservoirs, improving the results obtained with classical approaches such as to consider reservoir outflow equal to that of the previous day. Among the different machine learning techniques analyzed, the NARX approach was the option that provided the best estimations on average.

Orlando García-Feal et al.

Status: open (until 29 Jul 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-171', Anonymous Referee #1, 22 Jun 2022 reply

Orlando García-Feal et al.

Orlando García-Feal et al.

Viewed

Total article views: 190 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
147 34 9 190 3 3
  • HTML: 147
  • PDF: 34
  • XML: 9
  • Total: 190
  • BibTeX: 3
  • EndNote: 3
Views and downloads (calculated since 17 Jun 2022)
Cumulative views and downloads (calculated since 17 Jun 2022)

Viewed (geographical distribution)

Total article views: 182 (including HTML, PDF, and XML) Thereof 182 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 03 Jul 2022
Download
Short summary
Extreme events have increased in the last decades, having a good estimation of the outflow of a reservoir can be an advantage for water management or early warning systems. This study analyses the efficiency of different machine learning techniques to predict reservoir outflow. The results obtained showed that the proposed models provided a good estimation of the outflow of the reservoirs, improving the results obtained with classical approaches.
Altmetrics