<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Air quality data prediction in urban area is of great significance to control air pollution and protect the public health. The prediction of the air quality in the monitoring station is well studied in existing researches. However, air-quality-monitor stations are insufficient in most cities and the air quality varies from one place to another dramatically due to complex factors. A novel model is established in this paper to estimate and predict the Air Quality Index (AQI) of the areas without monitoring stations in Nanjing. The proposed model predicts AQI in a non-monitoring area both in temporal dimension and in spatial dimension respectively. The temporal dimension model is presented at first based on the enhanced k-Nearest Neighbor (KNN) algorithm to predict the AQI values among monitoring stations, the acceptability of the results achieves 92% for one-hour prediction. Meanwhile, in order to forecast the evolution of air quality in the spatial dimension, the method is utilized with the help of Back Propagation neural network (BP), which considers geographical distance. Furthermore, to improve the accuracy and adaptability of the spatial model, the similarity of topological structure is introduced. Especially, the temporal-spatial model is built and its adaptability is tested on a specific non-monitoring site, Jiulonghu Campus of Southeast University. The result demonstrates that the acceptability achieves 73.8% on average. The current paper provides strong evidence suggesting that the proposed non-parametric and data-driven approach for air quality forecasting provides promising results.</p></abstract>

Air quality data prediction in urban area is of great significance to control air pollution and protect the public health. The prediction of the air quality in the monitoring station is well studied in existing researches. However, air-quality-monitor stations are insufficient in most cities and the air quality varies from one place to another dramatically due to complex factors. A novel model is established in this paper to estimate and predict the Air Quality Index (AQI) of the areas without monitoring stations in Nanjing. The proposed model predicts AQI in a non-monitoring area both in temporal dimension and in spatial dimension respectively. The temporal dimension model is presented at first based on the enhanced k-Nearest Neighbor (KNN) algorithm to predict the AQI values among monitoring stations, the acceptability of the results achieves 92% for one-hour prediction. Meanwhile, in order to forecast the evolution of air quality in the spatial dimension, the method is utilized with the help of Back Propagation neural network (BP), which considers geographical distance. Furthermore, to improve the accuracy and adaptability of the spatial model, the similarity of topological structure is introduced. Especially, the temporal-spatial model is built and its adaptability is tested on a specific non-monitoring site, Jiulonghu Campus of Southeast University. The result demonstrates that the acceptability achieves 73.8% on average. The current paper provides strong evidence suggesting that the proposed non-parametric and data-driven approach for air quality forecasting provides promising results.

Data-Driven Temporal-Spatial Model for the Prediction of AQI in Nanjing

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Data-Driven Temporal-Spatial Model for the Prediction of AQI in Nanjing"}

Air quality data prediction in urban area is of great significance to control air pollution and protect the public health. The prediction of the air quality in...

Data-Driven Temporal-Spatial Model for the Prediction of AQI in Nanjing

Published Online: Jun 15, 2020

Page range: 255 - 270

Received: Mar 18, 2020

Accepted: May 05, 2020

DOI: https://doi.org/10.2478/jaiscr-2020-0017

Keywords
Air quality prediction, k-Nearest Neighbor, BP neural network, Non-monitoring stations

© 2020 Xuan Zhao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Data-Driven Temporal-Spatial Model for the Prediction of AQI in Nanjing

Published Online: Jun 15, 2020

Page range: 255 - 270

Received: Mar 18, 2020

Accepted: May 05, 2020

DOI: https://doi.org/10.2478/jaiscr-2020-0017

KeywordsAir quality prediction, k-Nearest Neighbor, BP neural network, Non-monitoring stations

© 2020 Xuan Zhao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Air quality prediction, k-Nearest Neighbor, BP neural network, Non-monitoring stations