Weather Prediction by Mass Neural Networks through an Integrated Model approach
Publication Date : 14/03/2016
The active and disordered nature of weather makes weather forecasting a challenging and provocative task. Various numerical models have been developed and applied for this purpose, however usually it’s difficult to conclude accurate predictions. Although artificial neural networks have been considerably applied for weather forecasting, even then marking the precise result was still a question! Consequently some researchers proposed to use Cluster models of neural networks for the prediction task. When considering multiple neural networks, the redundancy caused by having multiple models and also combining the results of different networks are still the main challenges. In this project, a new Composite model is proposed for weather forecasting based on the Cluster of neural networks. Here, the redundancy issue has been addressed by introducing an integrated model in which a feature selection module is first applied to the data. Introduction of a Mutual information approach to tackle the challenge of combining the results of different networks and reducing the redundancy in the Composite model. The evaluation of result presents an outperformance of the proposed method compared to previous work.
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