Study Of Prediction And Early Warning Of Hyphantria Cunea Based On Meteorological Factors

Fall webworm,Hyphantria cunea(Drury),is a foreign forest pest species,has been rapidly spread to the eastern China's eleven provinces and cities,seriously restricting the local epidemic area of ecological health and economic development.Based on this,this study mainly adopts the maximum entropy model and the association rule method,respectively,on the overall spatial distribution of the fall webworm in China,the spread and migration of the fall webworm under climate change and the influence of meteorological conditions on the occurrence degree of the fall webworm,and based on MVC framework to develop the prediction and early warning system.Following research results are achieved:(1)Based on the existing distribution information,the distribution range and risk area of fall webworm in China were predicted and analyzed based on the maximum entropy model,and the results of the test were tested and evaluated by using the Receiver Operating Characteristic curve(ROC curve).The results show that the spatial distribution of fall webworm in China is mainly concentrated in the most part of North China,the northern part of East China,the northern part of central China and the southern part of the Northeast China,Beijing,Jilin,Hebei,Anhui and other provinces are the high risk area of the occurrence of fall webworm.(2)Based on the analysis of the spread and migration o of fall webworm in China from 2041 to 2060,the Beijing Climate Center Climate System Model was selected to simulate the meteorological environment of the three typical concentrations in the future.The results showed that the meteorological environment of fall webworm.Results showed that the area of occurrence in China was significantly increased and showed a tendency to expand northward.(3)Based on the association rule algorithm,the discretized data of the record data and monitoring data related to meteorological stations were excavated.The results showed that there was a strong Relationship between the severity of the occurrence of the white moth and the different meteorological conditions,the mining results can form a pest occurrence knowledge base(4)Based on the MVC framework,the forecasting and early warning system was designed to realize the digital storage and information management of the historical data of the pest.The system realizes the visualization of the related prediction results of the occurrence of Hyphantria cunea insect pests,and realizes the real time warning according to the meteorological conditions.The system is simple and practical.