ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 39 Issue 3 > 2013,39(3):377-382. DOI:10.7519/j.issn.1000-0526.2013.03.013 Prev Next
Experimental Research on Thunderstorm Forecasting with Double Hidden Layer BP Neural Network: Case Study on Taiyuan
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Abstract:
A neural network scheme to do a multivariate analysis for forecasting the occurrence of thunderstorm in Taiyuan is presented by using sounding data and lightning location system data. Well correlated sounding factors are selected as the predictors， then all the input factors are normalized， and output data are adopted to two stage category so that the BP network with double hidden layers has been established and the independent samples can be tested in it. The results indicate that, in the same condition， compared with single hidden layer BP network, the double hidden layer BP network shows its advantage on solving classification problem. Compared with multivariate statistics regression algorithm, the neural network algorithm obtains higher thunderstorm forecasting TS score and more reliable results, showing good nonlinear processing ability in the thunderstorm forecasts based on sounding data. And then the rules of thunderstorm forecast results are analyzed and discussed, showing that sounding factors have a close connection with the occurrence of thunderstorm.
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