ABSTRACT

To improve the accuracy of forecasting the initial peak period of Dendrolimus punctatus (Walker) (Lepidoptera: Lasiocampidae) larvae, Bayes discriminant analysis, Stationary time series method and Weighted contingency table method were used to develop a prediction model for the initial peak period of the first generation of its larva based on occurrence data which spanned from 1983 to 2016 in Qianshan City, Anhui Province. Historical data were digitised, graded, programmed and used to build the forecast models. The predicted values were verified with data in 2017 and 2018. The historical coincidence rate from Bayes discriminant was 97.18%. In 2017, the forecasted value was level 1 and the actual level was level 2. In 2018, the forecasted and actual results were all level 1. The historical coincidence rate from the stationary time series was 82.76%, which indicated that the prediction result in 2017 was 1 level lower than the actual values, but was consistent in 2018. The historical coincidence rate from the weighted contingency table method was 93.94% and the forecasted results for 2017 and 2018 were consistent with the actual values. Among the three methods, Bayes discriminant method and weighted contingency table method gave better prediction results.

摘要

提高斑点松毛虫初始高峰期的预测精度(Walker) (Lepidoptera: Lasiocampidae) 幼虫、贝叶斯判别分析、平稳时间序列法和加权列联表法基于1983年的发生数据建立了其第一代幼虫初始高峰期的预测模型至2016年安徽省潜山市。历史数据被数字化、分级、编程并用于构建预测模型。预测值用2017年和2018年的数据进行了验证。贝叶斯判别式的历史符合率为97.18%。2017年预测值为1级，实际为2级。2018年预测值与实际结果均为1级。与平稳时间序列的历史吻合率为82.76%，表明2017年预测结果比实际值低1级，但与2018年一致。加权列联表法历史符合率为93.94%，2017年和2018年预测结果与实际值一致. 在这三种方法中，贝叶斯判别法和加权列联表法给出了较好的预测结果。