地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (7): 1132-1142.doi: 10.12082/dqxxkx.2019.180598
• 遥感科学与应用技术 • 上一篇
收稿日期:
2018-11-22
修回日期:
2019-03-06
出版日期:
2019-07-25
发布日期:
2019-07-25
作者简介:
作者简介:符海月(1977-),女,甘肃陇南人,博士,副教授,主要从事土地利用变化,遥感与GIS在人口、资源与环境中的应用研究。E-mail:
基金资助:
Received:
2018-11-22
Revised:
2019-03-06
Online:
2019-07-25
Published:
2019-07-25
Contact:
Haiyue FU
E-mail:fuhaiyue@njau.edu.cn
Supported by:
摘要:
合理构建PM2.5浓度预测模型是科学、准确地预测PM2.5浓度变化的关键。传统PM2.5预测EEMD-GRNN模型具有较好的预测精度,但是存在过于关注研究数据本身而忽略其物理意义的不足。本研究基于南京市2014-2017年PM2.5浓度时间序列数据,分析PM2.5浓度多尺度变化特征及其对气象因子和大气污染因子的尺度响应,基于时间尺度重构进行EEMD-GRNN模型的改进与实证研究。南京市样本数据PM2.5浓度变化表现为明显的天际尺度和月际尺度,从重构尺度(天际、月际)构建GRNN模型更具有现实意义;同时,PM2.5对PM10、NO2、O3、RH、MinT等因子存在多尺度响应效应,以其作为GRNN模型中的输入变量更具有时间序列上的解释意义。改进后的EEMD-GRNN模型具有更高的PM2.5浓度预测精度,MAE、MAPE、RMSE和R2分别为6.17、18.41%、8.32和0.95,而传统EEMD-GRNN模型的模型有效性检验结果分别为8.37、27.56%、11.56、0.91。对于高浓度天(PM2.5浓度大于100 μg/m3)的预测,改进模型更是全面优于传统EEMD-GRNN模型,MAPE为12.02%,相较于传统模型提高了9.03%。
符海月, 张祎婷. 时间尺度重构EEMD-GRNN改进模型预测PM2.5的研究[J]. 地球信息科学学报, 2019, 21(7): 1132-1142.DOI:10.12082/dqxxkx.2019.180598
Haiyue FU, Yiting ZHANG. Improving the EEMD-GRNN Model for PM2.5 Prediction based on Time Scale Reconstruction[J]. Journal of Geo-information Science, 2019, 21(7): 1132-1142.DOI:10.12082/dqxxkx.2019.180598
表1
南京市2014-2017年空气质量和气象数据的描述性统计"
单位 | 均值 | 标准差 | |
---|---|---|---|
PM2.5 | μg/m3 | 54.75 | 36.88 |
PM10 | μg/m3 | 96.36 | 55.21 |
SO2 | μg/m3 | 19.08 | 10.02 |
CO | mg/m3 | 0.99 | 0.34 |
NO2 | μg/m3 | 48.07 | 18.72 |
O3 | μg/m3 | 103.55 | 51.48 |
日平均风速(WS) | m/s | 2.68 | 1.79 |
日平均大气压(AP) | kPa | 100.60 | 0.88 |
每日地表空气相对湿度(RH) | % | 78.40 | 15.25 |
每日最高温度(MaxT) | ℃ | 20.86 | 8.98 |
每日最低温度(MinT) | ℃ | 13.41 | 8.89 |
每日总降水量(PR) | mm | 4.10 | 11.29 |
表3
PM2.5浓度与大气污染因子及气象因子周期对比"
周期/d | PM2.5 | 大气污染因子 | 气象因子 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PM10 | SO2 | CO | NO2 | O3 | WS | AP | RH | MaxT | MinT | PR | |||
天际尺度 | 3 | 3* | 3* | 3* | 3* | 3* | 3* | 4 | 3* | 3* | 3* | 3* | |
7 | 7* | 7* | 7* | 7* | 7* | 7* | 7* | 7* | 7* | 7* | 7* | ||
14 | 13 | 13 | 14* | 14* | 14* | 13 | 15 | 15 | 14* | 13 | 12 | ||
27 | 27* | 27* | 29 | 26 | 28 | 26 | 29 | 32** | 30 | 28 | 24 | ||
月际尺度 | 58 | 56 | 60 | 54 | 62 | 58* | 54 | 52 | 60 | 55 | 56 | 47 | |
172 | 133 | 122 | 162 | 162 | 139 | 112 | 417 | 162 | 325 | 417 | 97 | ||
417 | 417* | 417* | 325 | 417* | 417* | 195 | 584 | 417* | 487 | 974 | 172 | ||
974 | 974* | 731 | 584 | 974* | 974* | 417 | 974* | 974* | 1461 | 1461 | 417 | ||
2922 | 1461 | 1461 | 1461 | 1461 | 1461 | 1461 | 2922* | 1461 | 1461 | 2922* | 974 |
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