Abstract
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.
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Jiang, L., Wu, J. (2013). Hybird Evolutionary Algorithms for Artificial Neural Network Training in Rainfall Forecasting. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39068-5_44
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DOI: https://doi.org/10.1007/978-3-642-39068-5_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39067-8
Online ISBN: 978-3-642-39068-5
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