NDLI: An Improved Method on Meteorological Prediction Modeling using Genetic Algorithm and Artificial Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Long Jin Cai Yao Xiaoyan Huang
Copyright Year	2006
Description	Author affiliation: Guangxi Res. Inst. of Meteorol. Disasters Mitigation, Nanning (Long Jin; Cai Yao; Xiaoyan Huang)
Abstract	Aiming at forecasting typhoon tracks by using genetic algorithm and neural network approach, this paper presents a new prediction modeling scheme for selecting the structure of networks and determining the initial connection weight. The probability for obtaining a global optimal solution is raised by designing a strategy that the optimum individual for each generation is reserved after a certain number of generations in the computational process of genetic evolution. The case forecast results of the typhoon track over the South China sea area show that mean absolute error of the prediction during 1990-2003 is 150.0km form this new forecast model, and in comparison with the optimum individual form the last generation, under the conditions of the same predictors and period forecast error is 161.1km. Furthermore, it is also found that higher predictive accuracy form the forecast models using genetic algorithm and neural network approach, comparing the results to those form objective prediction technique of typhoon tracks and the climatology and persistence (CLIPER) methods
Starting Page	31
Ending Page	35
File Size	320595
Page Count	5
File Format	PDF
ISBN	1424403324
DOI	10.1109/WCICA.2006.1712356
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Meteorology Predictive models Genetic algorithms Artificial neural networks Typhoons Neural networks Electronic mail Weather forecasting Accuracy Intelligent control prediction modeling genetic algorithm neural network typhoon tracks
Content Type	Text
Resource Type	Article

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