Air quality estimation based on active learning and Kriging interpolation

Huijuan CHANG; Zhiwen YU; Zhiyong YU; Qi AN; Bin GUO

doi:10.11959/j.issn.2096-0271.2018061

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Air quality estimation based on active learning and Kriging interpolation

STUDY | 更新时间：2024-06-03

- Air quality estimation based on active learning and Kriging interpolation
- Big data research Vol. 4, Issue 6, Pages: 2018061(2018)
- 作者机构：
  
  1. 西北工业大学计算机学院，陕西西安 710072
  2. 福州大学数学与计算机科学学院，福建福州 350108
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2018061
  CLC： TP181
- Online First：2018-11，
  
  Published：15 November 2018
- 稿件说明：
移动端阅览
Huijuan CHANG, Zhiwen YU, Zhiyong YU, et al. Air quality estimation based on active learning and Kriging interpolation[J]. Big data research, 2018, 4(6): 2018061.
DOI：

Huijuan CHANG, Zhiwen YU, Zhiyong YU, et al. Air quality estimation based on active learning and Kriging interpolation[J]. Big data research, 2018, 4(6): 2018061. DOI： 10.11959/j.issn.2096-0271.2018061.

摘要

空气质量监测站仅能在少数位置部署，故而无法获取城市中每个位置的空气质量信息。提出了一种基于主动学习和克里金插值的空气质量推测算法。该算法首先选用克里金插值作为基础的空气质量推测算法，然后结合主动学习的思想，对置信度最大的位置进行优先采样，最终建立基于主动学习的插值模型，通过最少的监测点对空气质量进行采样，最大限度地提升推测其他位置空气质量的准确度。研究结果表明，所提算法能够有效地提高空气质量推测精度，同时减少监测站采样数量，降低部署成本。

Abstract

For the air quality monitoring station

it can only be deployed in a few locations and cannot obtain the air quality information of each location in the city.An air quality estimation algorithm based on active learning and Kriging interpolation was proposed.Firstly

Kriging interpolation was used as the basic air quality estimation algorithm.Secondly

combined with the idea of active learning

the position-first sampling with the highest confidence of the model was searched.Finally

an interpolation model based on active learning was established to select the least position-to-air quality.Sampling was performed to maximize the accuracy of air quality at other locations.The results show that the proposed algorithm can effectively improve the accuracy of air quality estimation

reduce the number of sampling stations and reduce the deployment cost.

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references

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