HVAQ: A High-Resolution Vision-Based Air Quality Dataset

Chen, Zuohui; Zhang, Tony; Chen, Zhuangzhi; Xiang, Yun; Qi, Xuan; Dick, Robert P.

doi:10.1109/tim.2021.3104415

Cited by 6 publications

(3 citation statements)

References 32 publications

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“…The dark channel is an informatcurreive feature for haze detection. It is defined as the minimum of all pixel colors in a local area and can be calculated using the following equation [31]:…”

Section: Haze-relevant Features Extractionmentioning

confidence: 99%

Airstacknet: A Stacking Ensemble-Based Approach for Air Quality Prediction

Ksibi¹,

Salhi²,

Alluhaidan³

et al. 2023

Computers, Materials &Amp; Continua

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The quality of the air we breathe during the courses of our daily lives has a significant impact on our health and well-being as individuals. Unfortunately, personal air quality measurement remains challenging. In this study, we investigate the use of first-person photos for the prediction of air quality. The main idea is to harness the power of a generalized stacking approach and the importance of haze features extracted from first-person images to create an efficient new stacking model called AirStackNet for air pollution prediction. AirStackNet consists of two layers and four regression models, where the first layer generates meta-data from Light Gradient Boosting Machine (Light-GBM), Extreme Gradient Boosting Regression (XGBoost) and CatBoost Regression (CatBoost), whereas the second layer computes the final prediction from the meta-data of the first layer using Extra Tree Regression (ET). The performance of the proposed AirStackNet model is validated using public Personal Air Quality Dataset (PAQD). Our experiments are evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Coefficient of Determination (R 2 ), Mean Squared Error (MSE), Root Mean Squared Logarithmic Error (RMSLE), and Mean Absolute Percentage Error (MAPE). Experimental Results indicate that the proposed AirStackNet model not only can effectively improve air pollution prediction performance by overcoming the Bias-Variance tradeoff, but also outperforms baseline and state of the art models.

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Section: Haze-relevant Features Extractionmentioning

confidence: 99%

Airstacknet: A Stacking Ensemble-Based Approach for Air Quality Prediction

Ksibi¹,

Salhi²,

Alluhaidan³

et al. 2023

Computers, Materials &Amp; Continua

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“…PM 2.5 levels at one location are affected by surrounding PM 2.5 levels, and PM 2.5 varies at small spatial scales [6]. Utilizing digital cameras and webcams is beneficial in estimating PM 2.5 concentrations at different areas of an image [7,8]. Also, images can capture external factors correlated with PM 2.5 levels; for example, images can track meteorological conditions such as humidity and cloud cover on its own.…”

Section: Introductionmentioning

confidence: 99%

“…Cameras (e.g., webcams) are less expensive and easier to maintain than most commonly used air quality sensors. Using images for PM 2.5 estimation typically increases field estimation MAE accuracy by 14.3% compared to only using particle sensors [8]. Moreover, existing research in visibility physics demonstrates significant correlation between visibility and PM 2.5 levels [9,10].…”

Section: Introductionmentioning

confidence: 99%

Image-Based Air Quality Forecasting Through Multi-Level Attention

Zhang

Dick

2022

2022 IEEE International Conference on Image Processing (ICIP)

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The problem of air quality forecasting is important but also challenging because air quality is affected by a diverse set of complex factors. This paper describes the first image-based air quality forecasting model. It fuses a history of PM 2.5 measurements with colocated images. We construct a multi-level attention-based recurrent network that uses images and PM 2.5 data to represent variation over space and time. Experiments on Shanghai data show that our model improves PM 2.5 RMSE prediction accuracy by 15.8% and MAE by 10.9% compared to previous forecasting methods. In addition, we evaluate the impact of each model component via ablation studies.

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