Forecasting of agricultural straw burning in the Northeastern China based on neural network
BAI Bing1, ZHAO Hong-mei1, ZHANG Su-mei2, ZHANG Xue-lei1, YANG Guang-yi1,3
1. Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China; 2. College of mining engineering of tut, Taiyuan University of Technology, Taiyuan 030024, China; 3. University of Chinese Academy of Sciences, Beijing 100049, China
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.
白冰, 赵红梅, 张素梅, 张学磊, 杨光义. 基于神经网络的东北地区秸秆焚烧预测[J]. 中国环境科学, 2020, 40(12): 5205-5212.
BAI Bing, ZHAO Hong-mei, ZHANG Su-mei, ZHANG Xue-lei, YANG Guang-yi. Forecasting of agricultural straw burning in the Northeastern China based on neural network. CHINA ENVIRONMENTAL SCIENCECE, 2020, 40(12): 5205-5212.
李莉莉,王琨,等.黑龙江省秸秆露天焚烧污染物排放清单及时空分布[J]. 中国环境科学, 2018,38(6):2055-2061. Li L L, Wang K, et al. Emission inventory and the temporal and spatial distribution of pollutant for open field straw burning in Heilongjiang province[J]. China Environmental Science, 2018,38(6):2055-2061.
[2]
王丽,李雪铭,许妍.中国大陆秸秆露天焚烧的经济损失研究[J]. 干旱区资源与环境, 2008,2:170-175. Wang L, Li X, Xu Y. The economic losses caused by crop residues burnt in open field in China[J]. Journal of Arid and Resources and Environment, 2008,2:170-175.
[3]
Chen Y, Li Q, Randerson J T, et al. The sensitivity of CO and aerosol transport to the temporal and vertical distribution of North American boreal fire emissions[J]. Atmos. Chem. Phys, 2009,(9):6559-6580.
[4]
Wang T J, Jiang F, Deng J J, et al. Urban air quality and regional haze weather forecast for Yangtze River Delta region[J]. Atmos. Environ, 2012,(58):70-83.
[5]
Di Giuseppe F, Remy S, Pappenberger F, et al. Improving Forecasts of Biomass Burning Emissions with the Fire Weather Index[J]. J. Appl. Meteorol. Clim, 2017,(56):2789-2799.
[6]
梁泽,李双成,等.耦合遗传算法与RBF神经网络的PM2.5浓度预测模型[J]. 中国环境科学, 2020,40(2):523-529. Liang Z, Li S C, et. A coupling model of genetic algorithm and RBF neural network for the prediction of PM2.5 concentration[J]. China Environmental Science, 2020,40(2):523-529.
[7]
Gardneral M W, Dorlingal S R. Artificial neural networks(the multilayer perceptron)—a review of applications in the atmospheric sciences[J]. Atmospheric Environment, 1998,32:2627-2636.
[8]
刘方,徐龙,马晓迅.BP神经网络的发展及其在化学化工中的应用[J]. 化工进展, 2019,6:2559-2573. Liu F, Xu L, Ma X X. Development of BP neural network and its application in chemistry and chemical engineering[J]. Chemical Industry and Engineering Progress, 2019,6:2559-2573.
[9]
胡超.基于BP人工神经网络的区域森林火灾预测研究[D]. 舟山:浙江海洋学院, 2015. Hu C. The regional forest fire research based on BP neural network[D]. Zhoushan:Zhejiang Ocean University, 2015.
[10]
Feng Y, Zhang W F, Sun D Z, et al. Ozone concentration forecast method based on genetic algorithm optimized back propagation neural networks and support vector machine data classification[J]. Atmospheric Environment, 2011,45(11):1979-1985.
[11]
Bai Y, Li Y, Wang X X, et al. Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions[J]. Atmospheric Pollution Research, 2016,7(3):557-566.
[12]
Mao X, Shen T, Feng X. Prediction of hourly ground-level PM2.5 concentrations 3days in advance using neural networks with satellite data in eastern China[J]. Atmospheric Pollution Research, 2017,8(6):1005-1015.
[13]
张恒德,张天航,等.基于BP神经网络的污染物浓度多模式集成预报[J]. 中国环境科学, 2018,38(4):1243-1256. Zhang H D, Zhang T H, et. Forecast of air quality pollutants' concentrations based on BP neural network multi-model ensemble method[J]. China Environmental Science, 2018,38(4):1243-1256.
[14]
Feng X, Fu T M, Cao H S, et al. Neural network predictions of pollutant emissions from open burning of crop residues:Application to air quality forecasts in southern China[J]. Atmospheric Environment, 2019,204:22-31.
[15]
张景源,杨绪红,涂心萌,等. 2014~2018年中国田间秸秆焚烧火点的时空变化[J]. 农业工程学报, 2019,35(19):191-199. Zhang J Y, Yang X H, Tu X M, et al. Spatio-temporal change of straw burning fire points in field of China from 2014 to 2018[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019,35(19):191-199.
[16]
Justice C O, Giglio L, Korontzi S, et al. The MODIS fire products[J]. Remote Sens Environ, 1983,(1/2):244-262.
[17]
Liu W B, Wang Z D, Liu X H. A survey of deep neural network architectures and their applications[J]. Neurocomputing, 2017,234(19):11-26.
[18]
周志华,曹存根.神经网络及其应用[M]. 北京:清华大学出版社, 2004:22-36. Zhou Z H, Cao C G. Neural network and its applications[M]. Beijing:Tsinghua University Press, 2004:22-36.
[19]
高宇航.一种改善BP神经网络性能的方法研究[J]. 微型机与应用, 2017,36(6):53-57,61. Gao Y H. A method of improving the performance of BP neural network[J]. Microcomputer and Application, 2017,36(6):53-57,61.
[20]
Satir O, Berberoglu S, Donmez C. Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem[J]. Natural Hazards and Risk, 2016,7(5):1645-1658.
[21]
金龙,况雪源,黄海洪,等.人工神经网络预报模型的过拟合研究[J]. 气象学报, 2004,62(1):62-70. Jin L, Kuang X Y, Huang H H, et al. Study on the overfittingof the artificial neural network forecasting model[J]. Acta Meteorologica Sinica, 2004,62(1):62-70.
[22]
Mason K, Duggan J, Howley E. Forecasting energy demand, wind generation and carbon dioxide emissions in Ireland using evolutionary neural networks[J]. Energy, 2018,155:705-720.
[23]
Maeda E E, Formaggio A R, Shimabukuro Y E, et al. Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks[J]. International Journal of Applied Earth Observation and Geoinformation, 2009,11(4):265-272.
[24]
Zhang G L, Wang M. Forest fire susceptibility modeling using a convolutional neural network for Yunnan Province of China[J]. International Journal of Disaster Risk Science volume, 2019,10:386-403.