Application of Deep Learning Models and Network Method for Comprehensive Air-Quality Index Prediction

Kim, Donghyun; Han, Heechan; Wang, Wonjoon; Kang, Yujin; Lee, Ho-Yong; Kim, Hung Soo

doi:10.3390/app12136699

Cited by 17 publications

(4 citation statements)

References 42 publications

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“…RMSE and MAE were used for performance evaluation, and overall, it was confirmed that the performance of the proposed model significantly improved. Kim et al (2022) used the LSTM and DNN models to predict the Comprehensive Air-Quality Index (CAI). Moreover, network techniques were applied to improve the performance of the model.…”

Section: Related Studiesmentioning

confidence: 99%

Concentration Separation Prediction Model to Enhance Prediction Accuracy of Particulate Matter

Jung

2023

Journal of Information and Communication Technology

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Demand for more accurate particulate matter forecasts is accumulating owing to the increased interest and issues regarding particulate matter. Incredibly low concentration particulate matter, which accounts for most of the overall particulate matter, is often underestimated when a particulate matter prediction model based on machine learning is used. This study proposed a concentration-specific separation prediction model to overcome this shortcoming. Three prediction models based on Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM), commonly used for performance evaluation of the proposed prediction model, were used as comparative models. Root mean squared error (RMSE), mean absolute percentage error (MAPE), and accuracy were utilized for performance evaluation. The results showed that the prediction accuracy for all Air Quality Index (AQI) segments was more than 80 percent in the entire concentration spectrum. In addition, the study confirmed that the over-prediction phenomenon of single neural network models concentrated in the ‘normal’ AQI region was alleviated.

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Section: Related Studiesmentioning

confidence: 99%

Concentration Separation Prediction Model to Enhance Prediction Accuracy of Particulate Matter

Jung

2023

Journal of Information and Communication Technology

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“…Correlation analysis, which indicates the correlation between the observed data being measured at the station and the data predicted through the prediction model, is a method designed to quantitatively identify the relationship between two variables [21,25,40].…”

Section: Evaluating the Predictive Power Of The Modelmentioning

confidence: 99%

“…The square error divided by n is the mean square error (MSE), and the square root of the error is the root mean square error (RMSE). This is the normalized root mean square error (NRMSE), which standardizes mean square error and root mean square error [21,25,40,41].…”

Section: Evaluating the Predictive Power Of The Modelmentioning

confidence: 99%

A Study on Developing an AI-Based Water Demand Prediction and Classification Model for Gurye Intake Station

Kim,

Choi,

Kang

et al. 2023

Water

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Drought has significant impacts on both society and the environment, but it is a gradual and comprehensive process that affects a region over time. Therefore, non-structural measures are necessary to prepare and respond to the damage caused by drought in a flexible manner according to the stage of drought. In this study, an AI-based water demand prediction model was developed using deep neural network (DNN) and long short-term memory (LSTM) models. The model was trained from 2004 to 2015 and verified from 2016 to 2021. Model accuracy was evaluated using data, with the LSTM model achieving a correlation coefficient (CC) of 0.95 and normalized root mean square error (NRMSE) of 8.38, indicating excellent performance. The probability of the random variable X falling within the interval [a,b], as described by the probability density function f(x), was calculated using the water demand data. The cumulative distribution function was used to calculate the probability of the random variable being less than or equal to a specific value. These calculations were used to establish the criteria for each stage of the crisis alert system. Decision tree (DT) and random forest (RF) models, based on AI-based classification, were used to predict water demand at the Gurye intake station. The models took into account the impact of water demand from the previous day, as well as the effects of rainfall, maximum temperature, and average temperature. Daily water demand data from the Gurye intake station and the previous day’s rainfall, maximum temperature, and average temperature data from a nearby observatory were collected from 2004 to 2021. The models were trained on data from 2004 to 2015 and validated on data from 2016 to 2021. Model accuracy was evaluated using the F1-score, with the random forest model achieving a score of 0.88, indicating excellent performance.

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“…Air pollutants are characterized by a wide variety of types and high spatial and temporal variations, making it difficult to interpret data such as pollutant concentration trends and predictions using conventional statistical analysis methods (Du et al, 2019). Recently, there have been many attempts to interpret air pollutant data using machine learning techniques (Gilik et al, 2022;Kim et al, 2022a;Kim et al, 2022b;Kumar and Pande, 2023). Machine learning is a technology that can improve results by training data using specific algorithms, and it has the advantage of being relatively free from human errors that can occur during experiments or research, and can produce excellent results depending on the choice of data processing methods and analysis algorithms (Khanzode and Sarode, 2020).…”

Section: Introductionmentioning

confidence: 99%

Utilizing Machine Learning-based Classification Models for Tracking Air Pollution Sources: A Case Study in Korea

Choi,

Kang,

Kim

2024

Aerosol Air Qual. Res.

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Urbanization and industrialization pose significant challenges in promptly identifying and managing air pollution sources. The application of machine learning technology offers a promising solution to solve the issue. By analyzing multidimensional datasets containing a wide range of air pollutants, a machine learning approach has the potential to significantly improve air pollution management and facilitate source tracking. This study aims to comprehensively evaluate machine learning-based emission source classification models to provide insights into air pollution source tracking and management. Using 972 datasets consisting of five emission sources and 27 air pollutants, different classification models were implemented and subsequently compared: Random Forest (RF), Naïve Bayes Classifier (NBC), Support Vector Machine (SVM), Artificial Neural Network (ANN), and K-Nearest Neighbors (K-NN). The RF model was found to have better predictive performance than the other four models, achieving an accuracy of 0.9691 and a kappa value of 0.9537. Hydrogen chloride and acetaldehyde were the most important variables for classifying emission sources. The findings suggest the potential of machine learning techniques in addressing air pollution challenges, and the classifier model implemented in this study shows great promise for effective emission source identification.

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Application of Deep Learning Models and Network Method for Comprehensive Air-Quality Index Prediction

Cited by 17 publications

References 42 publications

Concentration Separation Prediction Model to Enhance Prediction Accuracy of Particulate Matter

Concentration Separation Prediction Model to Enhance Prediction Accuracy of Particulate Matter

A Study on Developing an AI-Based Water Demand Prediction and Classification Model for Gurye Intake Station

Utilizing Machine Learning-based Classification Models for Tracking Air Pollution Sources: A Case Study in Korea

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