DQ-MAN: A tool for multi-dimensional data quality analysis in IoT-based air quality monitoring systems

Buelvas, Julio; Munera, Danny; Gaviria, Natalia

doi:10.1016/j.iot.2023.100769

Cited by 5 publications

(1 citation statement)

References 23 publications

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“…The remaining categories, namely very unhealthy and dangerous, were not observed during the entire course of the study. The assessment of the air pollution standard index (ISPU) [12]- [16] conducted by KLHK RI is presently facilitated through the utilization of the air quality monitoring system (AQMS) [17]- [21]. KLHK RI disseminates real-time ISPU measurement results via the internet, employing the ISPU Mobile application to provide accessible information.…”

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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A data quality management framework for equipment failure risk estimation: Application to the oil and gas industry

Kang,

Al Masry,

Varnier

et al. 2024

Engineering Applications of Artificial Intelligence

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Optimising air quality prediction in smart cities with hybrid particle swarm optimization‐long‐short term memory‐recurrent neural network model

Dalal,

Lilhore,

Faujdar

et al. 2024

IET Smart Cities

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In smart cities, air pollution is a critical issue that affects individual health and harms the environment. The air pollution prediction can supply important information to all relevant parties to take appropriate initiatives. Air quality prediction is a hot area of research. The existing research encounters several challenges that is, poor accuracy and incorrect real‐time updates. This research presents a hybrid model based on long‐short term memory (LSTM), recurrent neural network (RNN), and Curiosity‐based Motivation method. The proposed model extracts a feature set from the training dataset using an RNN layer and achieves sequencing learning by applying an LSTM layer. Also, to deal with the overfitting issues in LSTM, the proposed model utilises a dropout strategy. In the proposed model, input and recurrent connections can be dropped from activation and weight updates using the dropout regularisation approach, and it utilises a Curiosity‐based Motivation model to construct a novel motivational model, which helps in the reconstruction of long short‐term memory recurrent neural network. To minimise the prediction error, particle swarm optimisation is implemented to optimise the LSTM neural network's weights. The authors utilise an online Air Pollution Monitoring dataset from Salt Lake City, USA with five air quality indicators for comparison, that is, SO2, CO, O3, and NO2, to predict air quality. The proposed model is compared with existing Gradient Boosted Tree Regression, Existing LSTM, and Support Vector Machine based Regression Model. Experimental analysis shows that the proposed method has 0.0184 (Root Mean Square Error (RMSE)), 0.0082 (Mean Absolute Error), 2002*109 (Mean Absolute Percentage Error), and 0.122 (R2‐Score). The experimental findings demonstrate that the proposed LSTM model had RMSE performance in the prescribed dataset and statistically significant superior outcomes compared to existing methods.

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DQ-MAN: A tool for multi-dimensional data quality analysis in IoT-based air quality monitoring systems

Cited by 5 publications

References 23 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

A data quality management framework for equipment failure risk estimation: Application to the oil and gas industry

Optimising air quality prediction in smart cities with hybrid particle swarm optimization‐long‐short term memory‐recurrent neural network model

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