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 24 h 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.
Highlights
Open field combustion is a widely used approach to eliminate crop residue from agricultural land
The accumulated precipitation in a 24-h period and straw open burning prohibition areas should have a great influence on crop residue open burning
This research compared the accuracy of natural factors and added anthropogenic factors to forecast crop residue fire points
Summary
Open field combustion is a widely used approach to eliminate crop residue from agricultural land. To reduce the effects of crop residue burning on the atmosphere and human health, the Chinese government has implemented regulations to prohibit field burning and to promote constructive alternatives for using the crop residue in energy production, soil amendments, and animal feed [5]. Straw Open Burning Limit Areas) [8,9] These human-activity-related factors have become a major challenge for forecasting crop residue fire points. Many types of neural networks have been developed, including the back propagation neural network (BPNN), radial basis function neural network and linear neural network Among these methods, the internal structure of the BPNN is the simplest, meaning that when large-scale data are processed, errors in single data points have a small impact on the overall forecasting result [19]. This study is one of the first to consider the influence of human factors to better understand and forecast fire probability
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