Air Quality Prediction Using Regression Models

Julfikar, S. K.; Ahamed, S. Niyas; Rehena, Zeenat

doi:10.1007/978-981-16-3067-5_19

Cited by 1 publication

(1 citation statement)

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“…In this regard, a classic approach to this problem is to apply conventional dynamic models to forecast the concentration of urban air pollutants. Recently, various data analytics methodologies have been proposed to predict air pollutants' density, such that the air quality of a selected area is forecasted using machine learning algorithms (for example: [1][2][3]); deep learning algorithms (for example: [4][5][6]); or a combination of multiple algorithms (for example [3] etc.). Such approaches for air quality forecasting rely heavily upon collecting pollutant concentrations at monitoring stations.…”

Section: Introductionmentioning

confidence: 99%

A Framework for the Estimation of Air Quality by Applying Meteorological Images: Colours-of-the-Wind (COLD)

Chan,

Matthews,

Munir

2023

Environments

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This paper presents a new framework, “colours-of-the-wind” (COLD), which is designed to estimate air quality based on images from meteorological cameras, data analytics techniques, and the application of deep learning. Existing air quality estimation systems mainly rely on physical monitoring stations, which are limited by the monitoring stations’ physical constraints. Instead of collecting data from environmental monitoring stations, COLD collects air quality data from meteorological cameras. This approach can collect data from any location where a camera could capture a reliable image, which is otherwise not collectable by the physical environmental monitoring station(s). This approach can also avoid bias due to the location of data collection. The system is evaluated by building a prototype based on the Air Quality Health Index from the Hong Kong Observatory. This is one of the unique data-analytic frameworks based on such air quality measurement. The COLD’s air quality estimation is also based on AQHI, the first machine learning-based estimation framework that generates AQHI as the proposed output. Experimental results suggest that the approach adopted by the COLD prototype is feasible and has some promising outcomes. The results also suggest possible parameters for the CNN model used for the training and analyses of the images.

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