Data-driven temporal-spatial model for the prediction of AQI in Nanjin

• Autorzy: Zhao Xuan , Song Meichen , Liu Anqi , Wang Yiming , Wang Tong , Cao Jinde

Identyfikatory

DOI

10.2478/jaiscr-2020-0017

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

air quality prediction; k-Nearest Neighbor; BP neural network; non-monitoring stations

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2020
• Tom: Vol. 10, No. 4
• Strony: 255--270
• Opis fizyczny: Bibliogr. 42 poz., rys.

Twórcy

autor

Zhao Xuan

• School of Mathematics, Southeast University, Nanjing 210096, P. R. China

autor

Song Meichen

• School of Mathematics, Southeast University, Nanjing 210096, P. R. China

autor

Liu Anqi

• School of Mathematics, Southeast University, Nanjing 210096, P. R. China

autor

Wang Yiming

• School of Mathematics, Southeast University, Nanjing 210096, P. R. China

autor

Wang Tong

• School of Mathematics, Southeast University, Nanjing 210096, P. R. China

autor

Cao Jinde

• School of Mathematics, Southeast University, Nanjing 210096, P. R. China

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).