Improving the predictive skills of hydrological models using a combinatorial optimization algorithm and artificial neural networks
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Improving the predictive skills of hydrological models using a combinatorial optimization algorithm and artificial neural networksData
2023Centro/Dpto/Entidade
Enxeñaría da Auga e do Medio Ambiente (GEAMA)Cita bibliográfica
Farfán, J. F., & Cea, L. (2023). Improving the predictive skills of hydrological models using a combinatorial optimization algorithm and artificial neural networks. Modeling Earth Systems and Environment, 9(1), 1103-1118. https://doi.org/10.1007/s40808-022-01540-1
Resumo
[Abstract:] Ensemble modelling is a numerical technique used to combine the results of a number of different individual models in order to obtain more robust, better-fitting predictions. The main drawback of ensemble modeling is the identification of the individual models that can be efficiently combined. The present study proposes a strategy based on the Random-Restart Hill-Climbing algorithm to efficiently build ANN-based hydrological ensemble models. The proposed technique is applied in a case study, using three different criteria for identifying the model combinations, different number of individual models to build the ensemble, and two different ANN training algorithms. The results show that model combinations based on the Pearson coefficient produce the best ensembles, outperforming the best individual model in 100% of the cases, and reaching NSE values up to 0.91 in the validation period. Furthermore, the Levenberg-Marquardt training algorithm showed a much lower computational cost than the Bayesian regularisation algorithm, with no significant differences in terms of accuracy.
Palabras chave
Ensemble model
Artificial neural networks
Hydrological model
Water resources
Artificial neural networks
Hydrological model
Water resources
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Atribución 3.0 España