The four models for forecasting the peak period of Dendrolimus punctatus (Lepidoptera: Lasiocampiade) for the second generation egg. Issue 7 (19th April 2021)

• Record Type: Journal Article
• Authors: Zhang, Nan; Qian, Guangjing; Zhang, Lin; Song, Xueyu; Zou, Yunding; Bi, Shoudong
• Is Part Of: Entomological research. Volume 51:Issue 7(2021)
• Page Start: 357
• Page End: 366
• Publication Date: 2021-04-19
• Subjects: Bayes discriminant analysis -- Dendrolimus punctatus -- peak period -- periodic distance method -- stationary time series -- stepwise regression model; Insects -- Korea -- Periodicals; Entomology -- Periodicals; 595.709519
• DOI: 10.1111/1748-5967.12518
• Languages: English
• ISSNs: 1738-2297

Abstract

Abstract: To improve the accuracy of forecasting the peak period of Dendrolimus punctatus, stationary time series, periodic distance method, stepwise regression model and the Bayes discriminant analysis were used. RSME value, kappa coefficient and accuracy were used as evaluation criteria to predict the peak period for the second generation egg of D. punctatus with over 33 years from 1983 to 2016 in Qianshan County, Anhui Province. The predictions of these models were verified in 2017 and 2018. The prediction of the stationary time model and Bayes discriminant analysis for 2017 was one level lower than the actual result and for 2018 was one level higher than the actual result, while the prediction of the periodic distance method was identical to the actual result for 2017 and greatly different from the actual result for 2018. The accuracy for stationary time series (RMSE = 0.92 kappa = 0.76) and periodic distance method (RMSE = 2.96, kappa = 0.81) from 1983 to 2018 were 87.88% and 85.71%, respectively. When taking into consideration the standard error was based on differential, the accuracy for the prediction of stepwise regression model (RMSE = 0.25, kappa = 1.00) from 1983 to 2018 was 100%. The accuracy of Bayes discriminant method (RMSE = 0.71, kappa = 0.96) was 97.14%. Comparatively speaking, the stepwise regression model and Bayes discriminant analysis method were better than the stationary time series and periodic distance method in RMSE value, kappa coefficient and accuracy. So they were relatively ideal forecast methods.