Association Model between Visual Feature and AQI Rank Using Lifelog Data

Vo, Phuong-Binh; Phan, Trong-Dat; Dao, Minh-Son; Zettsu, Koji

doi:10.1109/bigdata47090.2019.9005636

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…Personal air quality is a significant indicator during the evaluation of the air pollution impact on personal health [17]. Air Quality Index (AQI) is a rank indicator of air pollution, which is a very critical environmental impact on the public health [10]. Over the last 40 years, city-wide air quality prediction has been of interest [18].…”

Section: Related Workmentioning

confidence: 99%

“…The experimental findings found that CNN classifier fulfilled accuracy better than the traditional combination methods of feature extraction and classification. In [10] presented an association framework between AQI rank of lifelogging data and visual features. In the training phase, the visual data (environmental images) and lifelog numerical data (environmental AQI measurements) are used.…”

Section: Related Workmentioning

confidence: 99%

“…It enables the development of reliable image-based models that can provide insights into personal AQI. These models use image processing algorithms to extract features from first-person images and estimate the air quality based on these features [5,[9][10][11]. Despite the above Image-based prediction models having acceptable prediction performances, the use of a single model for prediction to deal with the randomness of air pollution concentrations often leads to poor model generalization [12].…”

Section: Introductionmentioning

confidence: 99%

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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: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Ksibi¹,

Salhi²,

Alluhaidan³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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IMAGE-2-AQI: Aware of the Surrounding Air Qualification by a Few Images

Dao

Zettsu

Kiran

2021

Lecture Notes in Computer Science

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Deep learning based multimodal urban air quality prediction and traffic analytics

Hameed,

Islam,

Ahmad

et al. 2023

Sci Rep

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Urban activities, particularly vehicle traffic, are contributing significantly to environmental pollution with detrimental effects on public health. The ability to anticipate air quality in advance is critical for public authorities and the general public to plan and manage these activities, which ultimately help in minimizing the adverse impact on the environment and public health effectively. Thanks to recent advancements in Artificial Intelligence and sensor technology, forecasting air quality is possible through the consideration of various environmental factors. This paper presents our novel solution for air quality prediction and its correlation with different environmental factors and urban activities, such as traffic density. To this aim, we propose a multi-modal framework by integrating real-time data from different environmental sensors and traffic density extracted from Closed Circuit Television footage. The framework effectively addresses data inconsistencies arising from sensor and camera malfunctions within a streaming dataset. The dataset exhibits real-world complexities, including abrupt camera or station activations/deactivations, noise interference, and outliers. The proposed system tackles the challenge of predicting air quality at locations having no sensors or experiencing sensor failures by training a joint model on the data obtained from nearby stations/sensors using a Particle Swarm Optimization (PSO)-based merit fusion of the sensor data. The proposed methodology is evaluated using various variants of the LSTM model including Bi-directional LSTM, CNN-LSTM, and Convolutions LSTM (ConvLSTM) obtaining an improvement of 48%, 67%, and 173% for short-term, medium-term, and long-term periods, respectively, over the ARIMA model.

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Association Model between Visual Feature and AQI Rank Using Lifelog Data

Cited by 4 publications

References 13 publications

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

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

IMAGE-2-AQI: Aware of the Surrounding Air Qualification by a Few Images

Deep learning based multimodal urban air quality prediction and traffic analytics

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