辽宁省松毛虫发生面积气象预报方法研究.PDFVIP

中国农学通报 2013,29(13):56-59 Chinese Agricultural Science Bulletin 辽宁省松毛虫发生面积气象预报方法研究 于文颖，纪瑞鹏，冯 锐，赵先丽，武晋雯，张淑杰，张玉书 （中国气象局沈阳大气环境研究所，沈阳110016） 摘 要：为了利用气象因子预测松毛虫的发生面积，基于辽宁省阜新县1983—2008 年气象资料与松毛虫 发生面积等资料进行相关分析，将筛选出的气象因子作为预报因子，通过多元回归法和人工神经网络法 对松毛虫发生面积进行模拟和预测。结果表明，与松毛虫发生面积显著相关的5 个气象因子包括：上一 年12 月平均最低温度、上一年11 月平均相对湿度、上一年9 月降水量、本年2 月降水量和本年3 月降水 量；人工神经网络法的模拟和预测精度均优于多元回归法，多元回归法的预测精度58.2% ，人工神经网 络法的预测精度为83.6% ；人工神经网络法更适用于辽宁地区松毛虫发生面积的预报。 关键词：松毛虫；气象因子；发生面积；预测预报 中图分类号：S167 文献标志码：A 论文编号：2012-3757 The Study on Meteorological Forecast Methods of Dendrolimus Occurrence Area in Liaoning Yu Wenying, Ji Ruipeng, Feng Rui, Zhao Xianli, Wu Jinwen, Zhang Shujie, Zhang Yushu (InstituteofAtmosphericEnvironment,ChinaMeteorologicalAdministration, Shenyang 110016) Abstract: In order to forecast the occurrence area of Dendrolimus by using meteorological factors, the author analyzed the relationships between meteorological factors and occurrence area of Dendrolimus with the data from 1983 to 2008 in Fuxin County, Liaoning. The occurrence area of Dendrolimus was simulated and predicted on the selected meteorological factors as forecast factors, using multiple element regression and artificial neural network methods. The results showed that: 5 meteorological factors were significantly correlated with the occurrence area, including the mean minimum temperature of preceding December, the mean relative humidity of preceding November, the precipitation of preceding September, the precipitation of current February and current March. The simulation and prediction accuracy rate of the artificial neural network method was better than that of the multiple element regression