Genetic Algorithms Applied to Optimize Neural Network Training in Reference Evapotranspiration Estimation DOI Creative Commons
Eluã Ramos Coutinho, Jonni Guiller Ferreira Madeira,

Robson Mariano da Silva

et al.

Revista Brasileira de Meteorologia, Journal Year: 2025, Volume and Issue: 40

Published: Jan. 1, 2025

Abstract The increased consumption of natural resources, such as water, has become a global concern. Consequently, determining information that can minimize water consumption, evapotranspiration, is increasingly necessary. This research evaluates the capacity Genetic Algorithms (GAs) in training and fine-tuning parameters Artificial Neural Networks (ANNs) (MLP-GA) to obtain daily values reference evapotranspiration (ETo) accordance with Penman-Monteith FAO-56 method. method employed estimate ETo at 14 weather stations Brazil. findings are assessed based on coefficient correlation (r), mean absolute error (MAE), root square (RMSE), percentage (MPE), contrasted Hargreaves-Samani, Jensen-Haise, Linacre, Benavides & Lopez, Hamon methods, along Multilayer Perceptron (MLP) neural network, which conventionally trained employs hyperparameter tuning techniques Grid Search (MLP-GRID) Random (MLP-RD). results show MLP-GA is, average, 12 times faster than MLP-RD 60 MLP-GRID, while achieving highest precision indices most regions, an r 0.99, MAE ranging from 0.11 mm 0.20 mm, RMSE between 0.14 0.27 MPE 2.49% 7.09%. These suggest generated achieve 92.91% 97.51% comparison confirms employing (GA) automate optimization model effective enhances network's predict ETo.

Language: Английский

Genetic Algorithms Applied to Optimize Neural Network Training in Reference Evapotranspiration Estimation DOI Creative Commons
Eluã Ramos Coutinho, Jonni Guiller Ferreira Madeira,

Robson Mariano da Silva

et al.

Revista Brasileira de Meteorologia, Journal Year: 2025, Volume and Issue: 40

Published: Jan. 1, 2025

Abstract The increased consumption of natural resources, such as water, has become a global concern. Consequently, determining information that can minimize water consumption, evapotranspiration, is increasingly necessary. This research evaluates the capacity Genetic Algorithms (GAs) in training and fine-tuning parameters Artificial Neural Networks (ANNs) (MLP-GA) to obtain daily values reference evapotranspiration (ETo) accordance with Penman-Monteith FAO-56 method. method employed estimate ETo at 14 weather stations Brazil. findings are assessed based on coefficient correlation (r), mean absolute error (MAE), root square (RMSE), percentage (MPE), contrasted Hargreaves-Samani, Jensen-Haise, Linacre, Benavides & Lopez, Hamon methods, along Multilayer Perceptron (MLP) neural network, which conventionally trained employs hyperparameter tuning techniques Grid Search (MLP-GRID) Random (MLP-RD). results show MLP-GA is, average, 12 times faster than MLP-RD 60 MLP-GRID, while achieving highest precision indices most regions, an r 0.99, MAE ranging from 0.11 mm 0.20 mm, RMSE between 0.14 0.27 MPE 2.49% 7.09%. These suggest generated achieve 92.91% 97.51% comparison confirms employing (GA) automate optimization model effective enhances network's predict ETo.

Language: Английский

Citations

0