北京林业大学学报2017，Vol.39Issue(1)：85-93,9.DOI:10.13332/j.1000-1522.20160205
基于机器学习的落叶松毛虫发生面积预测模型
Studies on prediction models of Dendrolimus superans occurrence area based on machine learning
摘要
Abstract
Dendrolimus superans is one of the major forest pest insects, and its occurrence causes serious reductions in forest growth and significant threats to the safety of forest resources in China. Therefore, it is critical and necessary to predict the D. superans occurrence trend and population dynamics timely and accurately. Many factors affect the occurrence and outbreaks of pests, most likely involved in complex nonlinear systems. Unfortunately, most traditional models were based on linear prediction with very poor forecasting accuracy. In this study, following four variables, evaporation in mid March of current year, the average minimum temperature in early July of previous year, the extreme minimum temperature in late March of current year and the average wind speed in early November of previous year, were chosen as the independent variables, whereas the insect pest occurrence area was selected as the dependent variable. Three machine learning algorithms, i. e. multilayer feed-forward neural networks ( MLFN ) , general regression neural network (GRNN), and support vector machine (SVM) were used to predict the D. superans occurrence areas, and these prediction results were compared with those predicted by the traditional multiple linear regression method. Results showed that the prediction efficacies of the machine learning methods were largely superior to multiple linear regression prediction; with the support vector machine model being the best, reaching 100% prediction accuracy with a permissible error range of 30%tolerance, and with low RMSE value (0. 077) and short training time (1 second). These results suggest that machine learning algorithms, especially the support vector machine model, might have a great potential for accurate and effective predictions of insect pest occurrence areas as a reliable prediction tool.关键词
虫害预测/预测模型/多元线性回归/机器学习Key words
insect pest forecast/prediction model/multiple linear regression/machine learning分类
农业科技引用本文复制引用
张文一,景天忠,严善春..基于机器学习的落叶松毛虫发生面积预测模型[J].北京林业大学学报,2017,39(1):85-93,9.基金项目
东北林业大学学术名师支持计划(010602071)。 （010602071）
