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- W2896739218 abstract "Precipitation is viewed as a complex phenomenon that influences the efficiency of the agricultural season. In this paper, an ensemble of neural networks has been created and optimized to estimate monthly rainfall for Innisfail, Australia. The proposed ensemble utilizes single neural networks as components and combines them using a neural network fusion method. A novel ensemble components selection approach has been proposed and deployed. Ensemble components were selected based on a hybrid Genetic Algorithm (GA) that combines standard GA with particle swarm optimization algorithm. Various statistical measurements were calculated to assess the accuracy of the proposed ensembles against single neural networks, climatology and ensembles generated through an alternative selection approach. A better performance was obtained with the proposed ensembles when compared to alternative models." @default.
- W2896739218 created "2018-10-26" @default.
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- W2896739218 date "2018-07-01" @default.
- W2896739218 modified "2023-10-14" @default.
- W2896739218 title "A Novel Approach for Optimizing Ensemble Components in Rainfall Prediction" @default.
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- W2896739218 doi "https://doi.org/10.1109/cec.2018.8477739" @default.
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