NDLI: Hybrid model for weather forecasting using ensemble of neural networks and mutual information

Content Provider	IEEE Xplore Digital Library
Author	Ahmadi, A. Zargaran, Z. Mohebi, A. Taghavi, F.
Copyright Year	2014
Description	Author affiliation: Iranian Res. Inst. for Inf. Sci. & Technol., Tehran, Iran (Mohebi, A.) \|\| Univ. of Econ. Sci., Tehran, Iran (Zargaran, Z.) \|\| Amirkabir Univ. of Technol., Tehran, Iran (Ahmadi, A.) \|\| Univ. of Tehran, Tehran, Iran (Taghavi, F.)
Abstract	The chaotic and dynamic nature of weather makes weather forecasting a challenging and controversial task. Various numerical models have been developed and applied for this purpose, however usually they do not provide accurate predictions. Although artificial neural networks have been considerably applied for weather forecasting, they are not able to provide precise results. Consequently some researchers proposed to use ensemble 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 paper we propose a new hybridmodel for weather forecasting, which is based on an ensemble of neural networks. We address the redundancy issue by introducing a modular model in which a feature selection module is first applied to the data. We also, introduce a mutual information approach to tackle the challenge of combining the results of different networks and reducing the redundancy in the hybrid model. The evaluation results presented at the end of paper shows an outperformance of the proposed method compared to the similar methods in the literature.
Sponsorship	IEEE Geosci. Remote Sens. Soc.
Starting Page	3774
Ending Page	3777
File Size	295444
Page Count	4
File Format	PDF
ISBN	9781479957750
DOI	10.1109/IGARSS.2014.6947305
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-13
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Weather forecasting Mutual information Predictive models Numerical models Data models modular neural network Weather prediction neural network mutual information
Content Type	Text
Resource Type	Article

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