Can neural networks forecast open field burning of crop residue in regions with anthropogenic management and control? A case study in northeastern China

Abstract

Open burning is often used to remove crop residue during the harvest season. Despite a series of regulations by the Chinese government, the open burning of crop residue still frequently occurs in China, and the monitoring and forecasting crop fires have become a topic of active research. In this paper, crop fires in Northeastern China were forecasted using an artificial neural network (ANN) based on moderate-resolution imaging spectroradiometer (MODIS) satellite fire data from 2013–2020. Both natural factors (meteorological, soil moisture content, harvest date) and anthropogenic factors were considered. The model’s forecasting accuracy under natural factors reached 77.01% during 2013–2017. When considering the influence of anthropogenic management and control policies, such as the straw open burning prohibition areas in Jilin Province, the accuracy of the forecast results for 2020 was reduced to 60%. Although the forecasting accuracy was lower than for natural factors, the relative error between the observed fire points and the back propagation neural network (BPNN) forecasting results was acceptable. In terms of influencing factors, air pressure, the change in soil moisture content in a 24h period and the daily soil moisture content were significantly correlated with open burning. The results of this study improve our ability to forecast agricultural fires and provide a scientific framework for regional prevention and control of crop residue burning.