中文关键词PM_2.5人工神经网络支持向量机地理空间分析四大典型区 英文关键词PM_2.5backward artificial neural networksupport vector regressiongeospatial analysisfour typical regions

作者	单位	E-mail
罗毅	云南师范大学信息学院, 昆明 650500 西部资源环境地理信息技术教育部工程研究中心, 昆明 650500	luoyi861030@163.com
邓琼飞	云南师范大学信息学院, 昆明 650500 西部资源环境地理信息技术教育部工程研究中心, 昆明 650500
杨昆	云南师范大学信息学院, 昆明 650500 西部资源环境地理信息技术教育部工程研究中心, 昆明 650500	kmdcynu@163.com
杨扬	云南师范大学信息学院, 昆明 650500 西部资源环境地理信息技术教育部工程研究中心, 昆明 650500
商春雪	云南师范大学教务处, 昆明 650500
喻臻钰	云南师范大学信息学院, 昆明 650500 西部资源环境地理信息技术教育部工程研究中心, 昆明 650500

中文摘要 近20年来PM_2.5污染严重制约了中国可持续发展.长时间序列历史监测数据的缺失阻碍了相关研究.为此，本文以四大典型区域2013~2016年的PM_2.5浓度监测值和2000~2016年MODIS AOD数据、边界层高度、温度等气象数据作为基础数据，将反向人工神经网络和支持向量回归机两种算法相结合，构建组合模拟模型，并利用地理空间分析技术实现近20年来PM_2.5浓度历史变化过程的情景再现.研究结果表明，组合模型具有较低的误差和更高的泛化能力；时空分析结果表明，2000~2010年京津冀和东三省PM_2.5浓度持续增长，珠三角PM_2.5浓度缓慢下降，3个研究区PM_2.5污染范围呈扩大趋势，长三角PM_2.5浓度值及污染范围基本保持稳定.2012年4个研究区PM_2.5浓度值降低且污染范围缩小，但2013~2016年PM_2.5浓度略微上升后又下降，高污染范围缩小，这与国家采取PM_2.5区域联防等治理措施有关. 英文摘要 Two decades of PM_2.5 pollution has seriously hindered China's sustainable development. However, relevant research of PM_2.5 has been hindered because of the lack of long-term historical monitoring data. Therefore, ground observations of PM_2.5 concentration from 2013 to 2016 in four typical regions of China and the MODIS aerosol optical thickness data, boundary layer height, temperature, and other meteorological data from 2000 to 2016 were used as the basic data. A combined simulation model was constructed by combining the two algorithms of backward artificial neural network and support vector regression and obtains the PM_2.5 concentration history for the past 20 years using geospatial analysis technology. The results demonstrate that the combination model is better than the single model, with lower error and higher generalization ability. The spatial-temporal analysis results show that the concentration of PM_2.5 continued to increase in the Beijing-Tianjin-Hebei region and in the three northeastern provinces of China, the PM_2.5 concentration decreased slowly in the Pearl River Delta, the pollution range of PM_2.5 in three of the research areas showed an expanding trend, and the PM_2.5 concentration and pollution range remained stable in the Yangtze River Delta. In 2012, the concentration of PM_2.5 in the four study areas decreased and the pollution range narrowed, but the PM_2.5 concentration rose slightly after that decline and the high pollution range narrowed during 2013-2016, which with the country to take PM_2.5 regional defense and other governance measures.

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