Forecasting wildfire disease on tobacco: toward developing a high-accuracy prediction model for disease index using local climate factors and support vector regression

Abstract

Tobacco wildfire disease is common globally, and climate change may increase the risk of outbreaks. Therefore, there is an urgent need to establish an effective climate model to forecast the occurrence of wildfire disease. To design such a model, we collected data for 40 wildfire disease indices via tobacco field surveys and data for 15 climate factors of Guiyang County in China from 2012 to 2016. First, we built multiple linear regression (MLR), stepwise linear regression (SLR) and support vector regression (SVR) models using three climate features (precipitation, mean daily temperature and sunshine duration), and we could not find an effective model. Second, we built three corresponding models using expanded 15 climate features and an in-house WDEM method (the worst descriptor elimination multi-roundly), and the independent test results showed that the best SVR model had not only a higher predictive accuracy ( $$ {Q}_{ext}^2 $$ = 0.94) but also a better stability. Finally, we further evaluated the biological significance of their retained climate features and the single-factor effects of the best model according to the interpretability analysis, and our results indicated that (1) the three climate factors (minimum value of wind velocity, daily range of temperature and daily pressure) strongly affected the occurrence of wildfire disease; (2) the ranges of relative humidity and sunshine hours were negatively correlated with the occurrence of wildfire disease, while daily mean vapour pressure was positively correlated with the occurrence of the disease. Our work enables a useful theoretical prediction for wildfire disease, especially in terms of climate-related predictions.