Prediction of Rainfall in Da-Dong-Yong Hydrologic Station Based on Wavelet Neural Network

Xiu Li Sang; Yu Zhen Su; Han Jie Xiao; Hua Wang; Jian Xin Xu

doi:10.4028/www.scientific.net/AMR.726-731.3279

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 726-731Prediction of Rainfall in Da-Dong-Yong Hydrologic...

Prediction of Rainfall in Da-Dong-Yong Hydrologic Station Based on Wavelet Neural Network

Abstract:

This paper aims to seek a way for improving rainfall prediction accuracy from the perspective of time unit points which are 5 days, 10 days, 15 days, 20 days, 25 days, and 30 days. Based on the daily rainfall data from 2001 to 2010 of Da-dong-yong hydrologic station, the rainfalls are predicted by establishing the model of wavelet neural network. Results show that prediction accuracy and stability of time unit points is 30-day > 25-day > 15-day > 10-day > 20-day > 5-day. The trend of six kinds of rainfall forecast is consistent. When the number of forecast data is fewer and time unit point is longer, the accuracy and stability of rainfall forecast are better.

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Periodical:

Advanced Materials Research (Volumes 726-731)

Pages:

3279-3282

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.726-731.3279

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Online since:

August 2013

Authors:

Xiu Li Sang*, Yu Zhen Su, Han Jie Xiao, Hua Wang, Jian Xin Xu

Keywords:

Accuracy, Rainfall Prediction, Time Unit Point, Wavelet Neural Network (WNN)

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* - Corresponding Author

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