Air pollution significantly impacts global health and the environment, causing millions of deaths annually. With rapid urbanization and industrialization, especially in countries like China, air quality has deteriorat...
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ISBN:
(数字)9798331517601
ISBN:
(纸本)9798331517618
Air pollution significantly impacts global health and the environment, causing millions of deaths annually. With rapid urbanization and industrialization, especially in countries like China, air quality has deteriorated due to pollutants such as PM2.5, PM10, $\mathrm{CO}_{2}, \mathrm{CO}, \mathrm{NO}_{2}$, and SO 2 . Traditional models often fail to predict air quality accurately due to their dependence on optimal parameter sets and data availability. Machine learning (ML) methods, however, offer superior performance by handling large datasets and complex variable relationships. This systematic literature review examines 77 articles from 2020 to 2024, highlighting the most effective ML methods for air quality prediction, including Random Forest Regression (RFR), Gradient Boosting Regression (GBR), Support Vector Machines (SVM), Long Short-Term Memory (LSTM) networks, and LightGBM. The review discusses the types of data used, evaluation metrics, and the models’ performance. The findings emphasize the potential of ML to improve air quality monitoring and prediction, contributing to better public health and environmental management.
This study aims to predict the concentration of PM2.5 within wooden houses located in highland areas using advanced machine learning models, namely CatBoost, XGBoost, and LightGBM. These models are employed to analyze...
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ISBN:
(数字)9798331508616
ISBN:
(纸本)9798331508623
This study aims to predict the concentration of PM2.5 within wooden houses located in highland areas using advanced machine learning models, namely CatBoost, XGBoost, and LightGBM. These models are employed to analyze the impact of environmental factors, such as solar radiation temperature, wind speed, air temperature, humidity, and CO₂ concentration on indoor PM2.5 levels. The primary objective is to determine the model that provides the highest prediction accuracy, contributing to effective mitigation strategies for air quality management in similar environments. The findings demonstrate that the CatBoost model outperforms other models in terms of prediction accuracy, as evidenced by its lowest error metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). In contrast, the stacking model, which combines all three algorithms, did not enhance prediction accuracy and exhibited higher error values in extreme cases compared to individual models. This research offers valuable insights into the application of boosting algorithms for indoor air quality prediction, especially in highland wooden structures. The results are expected to aid future developments in air quality monitoring systems and provide a foundation for targeted public health interventions.
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