[1]黄 威,王星捷,阳清青.基于神经网络的雾霾预警系统研究与实现[J].计算机技术与发展,2019,29(10):26-30.[doi:10. 3969 / j. issn. 1673-629X. 2019. 10. 006]
　HUANG Wei,WANG Xing-jie,YANG Qing-qing.Research and Implementation of Haze Early Warning System Based on Neural Network[J].,2019,29(10):26-30.[doi:10. 3969 / j. issn. 1673-629X. 2019. 10. 006]
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基于神经网络的雾霾预警系统研究与实现()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年10期
页码:
26-30
栏目:
应用开发研究
出版日期:
2019-10-10

文章信息/Info

Title:
Research and Implementation of Haze Early Warning System Based on Neural Network
文章编号:
1673-629X(2019)10-0026-05
作者:
黄 威; 王星捷; 阳清青
成都理工大学 工程技术学院,四川 乐山 614007
Author(s):
HUANG Wei; WANG Xing-jie; YANG Qing-qing
Engineering Technical College,Chengdu University of Technology,Leshan 614007,China
关键词:
神经网络; 雾霾预警系统; 雾霾预测; 预警专题图; 插值算法
Keywords:
neural networks; haze warning system; haze prediction; early warning thematic map; interpolation algorithm
分类号:
TP302.1
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 10. 006
摘要:
传统的雾霾预警系统缺乏预知性,无法实现提前预警以规划预防,其次雾霾物理结构异常复杂且具有强烈非线性的结构、预测难度较高。 为了更好地反映雾霾在时间及空间的分布状况,为预防工作提供充足的时间准备,实现了一种预测与报警模型相结合的预警系统。 基于 BP 神经网络算法,对乐山市未来5 天各站点 PM2.5 含量进行了高精度的预测,并设计了各县及各城区预警的功能模块,对各县预警专题图、空间插值分布图及相关数据信息进行展示。 采用插值算法对市中区各城区监测点进行数据离散化处理,生成预警专题图及相关预测数据可视化。 系统以乐山市的 PM2.5 的监测站实时监测的数据为源数据,通过对乐山监测点历史 PM2.5 数据进行实验预测,并与实际值进行对比分析,达到了较好的预测效果。 建立的预警模型具有一定的实用和研究价值,可为相关部门提供可靠的数据支撑。
Abstract:
The traditional haze warning system lacks predictability and cannot realize early warning for planning and prevention. Secondly,the physical structure of haze is extremely complex and has a strong nonlinear structure,which is difficult to predict. In order to better reflect the distribution of haze in time and space and provide sufficient time preparation for prevention work,an early warning system combining prediction and alarm models is implemented. Based on the BP neural network algorithm,we have realized the high-precision prediction of PM2.5 content in each site in the next 5 days of Leshan City,and designed the functional modules of the early warning of each county and each city,and displayed the early warning thematic map and spatial interpolation distribution of each county, and related data information. The interpolation algorithm is used to discretize the data of the monitoring points in the urban areas of the city,and the early warning thematic map and related prediction data are generated. The system takes the real-time monitoring data of the monitoring station of Leshan City PM2.5 as the source data. Through the experimental prediction of the historical PM2. 5 data of the Leshan monitoring point,and the comparison and analysis with the actual value,a better prediction effect is achieved. The early warning model established has certain practical and research value,and can provide reliable data support for relevant departments.

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更新日期/Last Update: 2019-10-10