Application of spatial interpolation method in air quality monitoring based on RBF neural network

HUA Xiao-lei; TANG Hui-qiang; ZHANG Hong-yan; ZHANG Li-ping

doi:10.13873/J.1000-9787(2015)01-0157-04

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Application of spatial interpolation method in air quality monitoring based on RBF neural network

更新时间：2025-09-11

- Application of spatial interpolation method in air quality monitoring based on RBF neural network
- Vol. 34, Issue 1, Pages: 157-160(2015)
- 作者机构：
  
  1. 南京信息工程大学信息与控制学院
  2. 南京信息工程大学气象灾害预报预警与评估协同创新中心
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2015)01-0157-04
  CLC： X831;TP183
- Published：2015
- 稿件说明：
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[1]花晓蕾,唐慧强,张红燕,张丽萍.基于RBF神经网络的空间插值法在空气质量监测中的应用[J].传感器与微系统,2015,34(01):157-160.

HUA Xiao-lei, TANG Hui-qiang, ZHANG Hong-yan, et al. Application of spatial interpolation method in air quality monitoring based on RBF neural network[J]. 2015, 34(1): 157-160.
[1]花晓蕾,唐慧强,张红燕,张丽萍.基于RBF神经网络的空间插值法在空气质量监测中的应用[J].传感器与微系统,2015,34(01):157-160. DOI： 10.13873/J.1000-9787(2015)01-0157-04.

HUA Xiao-lei, TANG Hui-qiang, ZHANG Hong-yan, et al. Application of spatial interpolation method in air quality monitoring based on RBF neural network[J]. 2015, 34(1): 157-160. DOI： 10.13873/J.1000-9787(2015)01-0157-04.

摘要

针对我国现有大气监测站点数量有限且离散

采集的数据不能代表整个区域的空气质量等问题

提出基于RBF神经网络的空间插值法应用于空气质量的监测

以经纬度和邻近点污染物浓度为输入

建立插值点与地理坐标和邻近点之间的对应关系。实验结果表明:该方法具有较高的插值精度

为预测未知空间数据值提供了有效的处理方法

同时为大气污染治理和控制提供理论依据。

Abstract

Aiming at problem of quantity of atmospheric monitoring sites in our country is limited and positions are discrete

collecting data can not represent air quality of the entire area

interpolation model is established based on RBF netural network for air quality monitoring

longitude

latitude and pollutant concentrations of the vicinity stations are designed as the input of net

then build corresponding relation between interpolating point and geographic coordinate and neighborhood points. Experimental results show that the method has high interpolation precision and provide an effective processing method of predicting data for the place with unknown

therefore

treatment of air polution and control provide theory basic.

关键词

Keywords

references

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径向基神经网络的汇率预测模型研究 [J]. 廖薇,冯小兵,许春冬,刘锦高. 计算机工程与应用 . 2009(24)

城市空气质量GIS数据模型及分析系统的集成与应用 [J]. 廖永丰,张莉,王五一,王钦敏,杨林生. 地球信息科学 . 2007(01)

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基于RBF神经网络的PM 10 污染预测研究 [D]. 王兰霞. 西安建筑科技大学 2008

基于RBF神经网络的土壤属性信息空间插值方法研究 [D]. 李启权. 四川农业大学 2006

地理信息系统概论[M]. 高等教育出版社 , 黄杏元等编著, 2001

环境气象学与特种气象预报[M]. 气象出版社 , 吴兑,邓雪娇编著, 2001

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