Classification of the Air Quality Level based on Analysis of the Sky Images

Samsami, Mohammad Mehdi; Shojaee, Nooshin Sadat; Savar, S.; Yazdi, Mehran

doi:10.1109/iraniancee.2019.8786738

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…The outcomes of this study imply that the developed classification model is suitable for evaluating PM10 and PM2.5 air quality. Another inquiry conducted by Samsami et al [28] explored the categorization of air quality levels based on the scrutiny of sky images. This investigation utilized image analytical techniques and computer vision methods by extracting image features.…”

Section: Introductionmentioning

confidence: 99%

The development of color histogram method to identify air quality index based on sky images

Enggari,

Sumijan,

Tajuddin

2024

IJEECS

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Air quality index measurements in Indonesia are carried out by ministry of environment and forestry (KLHK). The Ministry divides air quality levels into 5 categories, namely good, moderate, unhealthy, very unhealthy and dangerous. In this study, 3 air quality categories were used as primary research data, namely good, moderate and unhealthy because the others, never occurred in Indonesia from the time this research was conducted until its completion. This research develops the color histogram method in order to recognize the shape of an object in an image. First stage in this research is inputting the sky image into the system. Then carry out pre-processing in the form of cropping the image to obtained is only an image of sky. Next, convert the red, green and blue (RGB) colored sky image to Grayscale, then image enhancement, then noise reduction. After that is processed using development of the color histogram method. Refinement of color histogram method has yielded an impressive accuracy level of 90%, validated through the analysis of 30 sky images. The method successfully detected 27 images accurately, while three images posed detection challenges. The findings of this research is color histogram method can be used to identify objects especially air pollution from sky images.

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Section: Introductionmentioning

confidence: 99%

The development of color histogram method to identify air quality index based on sky images

Enggari,

Sumijan,

Tajuddin

2024

IJEECS

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“…This emerging sensor-based air quality monitoring field can provide highdensity spatiotemporal pollution data, supplementing the established methodology with more precise and expensive devices [3]. The immense volume of collected spatiotemporal data has provided a better opportunity to apply machine learning (ML) techniques in air quality areas, such as air contaminant prediction [4], [5], missing data imputation [6], [7], and classification tasks [8].…”

Section: Introductionmentioning

confidence: 99%

TinyML Models for a Low-Cost Air Quality Monitoring Device

Wardana,

Fahmy,

Gardner

2023

IEEE Sens. Lett.

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“…In addition to the fixed scene, sky information of different scenes is also used to estimate AQI [ 21 ]. The sky area of the image can be used to classify AQI into six categories via the Gabor filter and K-NN classifier after calculating the transmittance [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Using High-Frequency Information and RH to Estimate AQI Based on SVR

Liaw

Chen

2021

Sensors

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The Environmental Protection Administration of Taiwan’s Executive Yuan has set up many air quality monitoring stations to monitor air pollution in the environment. The current weather forecast also includes information used to predict air pollution. Since air quality indicators have a considerable impact on people, the development of a simple, fast, and low-cost method to measure the AQI value is a worthy topic of research. In this study, a method was proposed to estimate AQI. Visibility had a clear positive relationship with AQI. When images and AQI were compared, it was easy to see that visibility decreased with the AQI value increase. Distance is the main factor affecting visibility, so measuring visibility with images has also become a research topic. Images with high and low PM2.5 concentrations were used to obtain regions of interest (RoI). The pixels in the RoI were calculated to obtain high-frequency information. The high-frequency information of RoI, RH, and true AQI was used for training via SVR, which was used to generate the model for AQI estimation. One year of experimental samples was collected for the experiment. Two indices were used to evaluate the performance of the proposed method. The results showed that the proposed method could be used to estimate AQI with acceptable performance in a simple, fast, and low-cost way.

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Classification of the Air Quality Level based on Analysis of the Sky Images

Cited by 6 publications

References 24 publications

The development of color histogram method to identify air quality index based on sky images

The development of color histogram method to identify air quality index based on sky images

TinyML Models for a Low-Cost Air Quality Monitoring Device

Using High-Frequency Information and RH to Estimate AQI Based on SVR

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