Research on Pine Wilt Disease Spread Prediction Based on an Improved Light Gradient Boosting Machine Model.

Pine wilt disease has caused significant damage to China's ecological and financial resources. To prevent its further spread across the country, proactive control measures are necessary. Given the low accuracy of traditional models, we have employed an enhanced light gradient boosting machine (LGBM) model to predict the development trend of pine wilt disease in China, providing a theoretical basis for its monitoring and prevention. We collected and organized data on the occurrence points of pine wilt disease at the county level in China. By incorporating anthropogenic factors such as the volume of pine wood imports from 2017 to 2022, the density of graded roads, the number of adjacent counties, and the presence of wood processing factories, as well as natural factors such as temperature, humidity, and wind speed, we employed Pearson correlation and LGBM model's feature importance analysis to select the 17 most significant influencing factors. Spatial analysis was conducted on the epidemic subcompartments (a divisional unit smaller than a township) of pine wilt disease for 2022 and 2023, revealing the distribution patterns of epidemic subcompartments within 2 km of roads and the spatial relationships between new and old epidemic subcompartments. We improved the LGBM model using a Bayesian algorithm, sparrow search algorithm, and hunter-prey optimization algorithm. By comparison, the enhanced model was validated to outperform in terms of accuracy, precision, recall, sensitivity, and specificity. Based on the results of correlation analysis and spatial analysis, an enhanced model was used to predict the emergence of pine wilt disease in new counties and districts in the future. Currently, pine wilt disease is primarily concentrated in the central-southern and northeastern provinces of China. Predictions indicate that the disease will further spread to the northeastern and southern regions of the country in the future.