Air pollution forecasting from sky images with shallow and deep classifiers

Vahdatpour, Mohammad Saleh; Sajedi, Hedieh; Ramezani, Farzad

doi:10.1007/s12145-018-0334-x

Cited by 24 publications

(11 citation statements)

References 25 publications

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“…The authors described the phases presented in Figure 4 in the following parts of this paper. The proposed method is a transitive method between the use of neural networks to predict air quality based on photos presented in publications [ 13 , 14 , 15 ] and the approach based on modeling numerical data presented by the authors in [ 16 , 23 , 24 ]. In the first step, texture analysis for each of the average photo frames has been performed, using three complementary methods.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…The assessment of air pollution with particulate matter is often based on images and images analysis. Papers presented models based on images analysis with Gabor filter [ 13 ], conversion to gray scale, and the Otsu method [ 18 ]. Liu et al also proposed using six image features: transmission, whole image and local image contrast, entropy, sky smoothness, and color.…”

Section: Conclusion Limitations and Future Researchmentioning

confidence: 99%

“…The main limitation in using CNN is a small dataset of images. In paper [ 13 ], the authors presented shallow (Random Forest) and deep classifiers (CNN) for five-class air quality assessment for an Air Quality Index (AQI). The authors used a method for multiple-scene images and obtained an AUC of 0.6 and an accuracy of 0.53.…”

Section: Conclusion Limitations and Future Researchmentioning

confidence: 99%

“…The literature mentions the use of artificial neural networks, SVM, spatial interpolation models, and statistical models for air quality modeling. In neural network-based methods, Vahdatpour et al proposed a method to estimate pollution forecast with Convolutional Neural Network based on sky images and Gabor transform [ 13 , 14 ]. Yang et al presented shallow ResNet with layer enhancement for PM 2.5 index prediction [ 15 ] based on image data from Beijing, Shanghai in China.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Application of Photo Texture Analysis and Weather Data in Assessment of Air Quality in Terms of Airborne PM10 and PM2.5 Particulate Matter

Chuchro

Sarlej

Grzegorczyk

et al. 2021

Sensors

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The study was undertaken in Krakow, which is situated in Lesser Poland Voivodeship, where bad PM10 air-quality indicators occurred on more than 100 days in the years 2010–2019. Krakow has continuous air quality measurement in seven locations that are run by the Province Environmental Protection Inspectorate. The research aimed to create regression and classification models for PM10 and PM2.5 estimation based on sky photos and basic weather data. For this research, one short video with a resolution of 1920 × 1080 px was captured each day. From each film, only five frames were used, the information from which was averaged. Then, texture analysis was performed on each averaged photo frame. The results of the texture analysis were used in the regression and classification models. The regression models’ quality for the test datasets equals 0.85 and 0.73 for PM10 and 0.63 for PM2.5. The quality of each classification model differs (0.86 and 0.73 for PM10, and 0.80 for PM2.5). The obtained results show that the created classification models could be used in PM10 and PM2.5 air quality assessment. Moreover, the character of the obtained regression models indicates that their quality could be enhanced; thus, improved results could be obtained.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Conclusion Limitations and Future Researchmentioning

confidence: 99%

Section: Conclusion Limitations and Future Researchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of Photo Texture Analysis and Weather Data in Assessment of Air Quality in Terms of Airborne PM10 and PM2.5 Particulate Matter

Chuchro

Sarlej

Grzegorczyk

et al. 2021

Sensors

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“…In [28] presented two frameworks to estimate the air pollution level. In the first framework, the images were preprocessed, the features extracted from the images using Gabor transform, and then two shallow classifiers are utilized for modeling and predicting the air pollution level.…”

Section: Related Workmentioning

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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Real‐Time Air Quality Index Detection through Regression‐Based Convolutional Neural Network Model on Captured Images

Sarkar,

Saha,

Saha

2024

Environmental Quality Mgmt

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Our research addresses the imperative need for an efficient air quality monitoring and forecasting system to mitigate the significant health risks of air pollution. Departing from conventional binary data collection methods, we employ image‐based techniques to overcome inherent limitations. A pioneering aspect of our work involves the development of a novel model capable of predicting six air pollutants (PM2.5, PM10, O3, CO, SO2, NO2) along with Air Quality Index for Bengaluru, Delhi, and Tamil Nadu, achieving a commendable mean absolute error of 0.1432 on the test set. The efficacy of our approach is validated through a meticulously curated dataset comprising approximately 5455 images. We emphasize the significance of normalization by presenting outputs before and after, shedding light on the impact of parameters with varying ranges and strategies employed to mitigate such discrepancies. A detailed analysis of our model's best and worst outputs provides valuable insights into its strengths and limitations. To enhance user accessibility, we introduce an innovative image‐based, real‐time, user‐friendly dashboard that allows users to conveniently assess a location's air pollution levels by uploading an image. This holistic approach offers a promising avenue for accurate air quality prediction and real‐time monitoring.

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Air pollution forecasting from sky images with shallow and deep classifiers

Cited by 24 publications

References 25 publications

Application of Photo Texture Analysis and Weather Data in Assessment of Air Quality in Terms of Airborne PM10 and PM2.5 Particulate Matter

Application of Photo Texture Analysis and Weather Data in Assessment of Air Quality in Terms of Airborne PM10 and PM2.5 Particulate Matter

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

Real‐Time Air Quality Index Detection through Regression‐Based Convolutional Neural Network Model on Captured Images

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