NDLI: The application of artificial neural network on forecast of meiyu rainfall at lower Yangtze River Valley

Content Provider	IEEE Xplore Digital Library
Author	Jianhong Wang Chunshengng Miao Zuocheng Yin Wenxiu Wei Yu Zhao Yunrong Wu
Copyright Year	2010
Description	Author affiliation: Bureau Heibei Meteorological administration, Shijiazhuang, China (Wenxiu Wei) \|\| Yancheng Meteorological Bureau, China (Yunrong Wu) \|\| Benxi Meteorological, Liaoning, China (Chunshengng Miao) \|\| College of Atmospheric Science, Nanjing University of Information Science & Technology, China (Jianhong Wang; Zuocheng Yin; Yu Zhao)
Abstract	ANN (the artificial neural network) is applied on the products from the numerical model of WRF (the Meso-Scale Weather Research Forecast Model) for forecasting categorical Rainfall over lower Yangtse river Valley during Meiyu season. The products are as the basic forecast factors, then they are united into combined factors with atmospheric dynamic, thermodynamic and humidity features. The combined factors are further selected into two groups by statistic testing methods. The factors are put into ANN in groups, the categorical rainfall forecasts are obtained through training procedures. The rainfall forecasts from ANN and the prediction of WRF model, and the forecasts by regression equations of the two group factors are compared each other. The real rainfalls as verified values in the comparison are recorded at the three typical stations of Nanjing , Yangzhou and Nantong that all located along the lower Yangtze River Valley. The results showed that the ANN can give better forecast of categorical rainfall during Meiyu season than the other two methods (WRF and regression), the missing and wrong forecast rates are reduced substantially, the accuracy rate of the categorical rain forecast increase over 10% , the accuracy rate of the heavy rain forecast is even higher, near 60% for the chosen cases. During the categorical forecast, the optimization of forecast factors is very important to a better ANN application.
Starting Page	1887
Ending Page	1891
File Size	332107
Page Count	5
File Format	PDF
ISBN	9781424459582
e-ISBN	9781424459612
DOI	10.1109/ICNC.2010.5584627
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	ANN(the artificial neural network) Rain Accuracy combined factors Weather forecasting the forecast of categorical Rainfall Artificial neural networks Rivers accuracy rate of forecast Forecasting
Content Type	Text
Resource Type	Article

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