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Issue:ISSN 2095-1353
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Your Position :Home->Past Journals Catalog->2018年55 No.4

The three models for forecasting the peak larval period of Dendrolimus punctatus
Author of the article:YU Yan1** WANG Zhen-Xing1 LI Shang2 ZHOU Xia-Zhi2*** BI Shou-Dong1*** FANG Guo-Fei3 ZHANG Guo-
Author's Workplace:YU Yan1** WANG Zhen-Xing1 LI Shang2 ZHOU Xia-Zhi2*** BI Shou-Dong1*** FANG Guo-Fei3 ZHANG Guo-Qing4 ZOU Yun-Ding2 ZHANG Zhen4 SONG Yu-Shuang3
Key Words: [Objectives] To scientifically determine the optimum period for controlling Dendrolimus punctatus, and thereby improve the effectiveness of current control methods. [Methods] Stationary time series, BP neural network and Markov Chain analysis were used to develop models predicting the timing of p
Abstract:

 [Objectives]  To scientifically determine the optimum period for controlling Dendrolimus punctatus, and thereby improve the effectiveness of current control methods. [Methods]  Stationary time series, BP neural network and Markov Chain analysis were used to develop models predicting the timing of peak larval abundance from 1983 to 2016 in Qianshan County Anhui Province. The predictions of these models were verified in 2015 and 2016. [Results]  The peak of the first larval generation was predicted to occur on June 5 and that of the second generation on September 6. The predicted dates were exactly the same as the actual dates in 2015 and 2016. The stationary time sequence method predicted the timing of peak of larval abundance with 96.77% accuracy from 1983 to 2014 if the error criterion was > 2 days. If the error criterion is less ≤1, the accuracy was 74.19%. The accuracy of the prediction of the second generation larval peak was the same as that for the first generation. For the BP neural network, if the error criterion was one day, the accuracy of the predicted outcome was 100% from 1983 to 2014. [Conclusion]  The stationary time sequence method was applicable to stable time series for predicting peak larval abundance whereas the Markov Chain was directly influenced by the accuracy of the prediction results. The BP Neural Network method can be used to determine nonlinear relationships between independent variables and predicted larval abundance, which is an ideal prediction method.

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