基于神经网络的东北地区秸秆焚烧预测

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Abstract The open agricultural burning produces large amount of air pollutants which contribute significantly to air pollution and climate change. Having vast areas of farmland, the environment of Northeastern China is impacted by the agricultural burning every year, especially during the harvest season. In order to further understand the open field burning activities and improve the prediction ability of it, in this study, the artificial neural network was introduced to conduct the forecasting of crop straw open field burning in Northeastern China. The results showed that the neural network model has a good prediction stability and it successfully reproduced the crop straw burning occurred in the Songnen Plain during the period from October 25 to November 15 2015, with an accuracy of 67.1%. After a series of tests, the forecasting accuracy of the neural network model improved, and could reach up to 69.7% when the ratio of training set to validation set was 80:20. In addition, according to the results of different research time periods, this neural network showed higher performance on the long-term prediction. Furthermore, comparing to with wind speed, precipitation, temperature and pressure, the relative humidity is the most important meteorological factor that affects straw open field burning. This study could not only help to improve the fire emission data used for the air quality forecasting model, but also provided technical support for government departments in controlling the open agriculture burning.

Key words： artificial intelligence fire forecasting biomass burning Northeast China

Received: 29 April 2020

PACS:

X831

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	Articles by authors
	BAI Bing
	ZHAO Hong-mei
	ZHANG Su-mei
	ZHANG Xue-lei
	YANG Guang-yi

Cite this article:

BAI Bing,ZHAO Hong-mei,ZHANG Su-mei等. Forecasting of agricultural straw burning in the Northeastern China based on neural network[J]. CHINA ENVIRONMENTAL SCIENCECE, 2020, 40(12): 5205-5212.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2020/V40/I12/5205

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