Anzeige
Erschienen in:
2013 | OriginalPaper | Buchkapitel
Hybird Evolutionary Algorithms for Artificial Neural Network Training in Rainfall Forecasting
verfasst von : Linli Jiang, Jiansheng Wu
Erschienen in: Advances in Neural Networks – ISNN 2013
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
This paper investigates the effectiveness of the Genetic Algorithm (GA) and Simulated Annealing algorithm (SA) training artificial neural network weights and biases for rainfall forecasting, namely GAS–ANN. Firstly, a hybrid GA and SA method is used to train the begining connection weights and thresholds of ANN. Secondly, the back propagation algorithm is used to search around the global optimum. Finally, a numerical example of monthly rainfall data in a catchment located in a subtropical monsoon climate in Linzhou of China, is used to elucidate the forecasting performance of the proposed GASA–ANN model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the autoregressive integrated moving average (ARIMA), back–propagation neural network (BP–NN) and pure Genetic Algorithm training Artificial Neural Network model (GA–ANN). Therefore, the GASA–ANN model is a promising alternative for rainfall forecasting.