ArticleInternational journal of environmental research and public health2023
PM2.5 Concentration Prediction Model: A CNN-RF Ensemble Framework.
Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Analyzing histopathological images using fused CNN features based on the geometric active contour method for early diagnosis of lung and colon cancer.Discover oncology · 2025Article
- Nationwide Machine Learning-Ensemble PMEnvironment & health (Washington, D.C.) · 2025Article
- PMScientific reports · 2025Article
- Article
- Application of the Lasso regularisation technique in mitigating overfitting in air quality prediction models.Scientific reports · 2025Article
- Multiresolution Analysis of HRRR Meteorological Parameters and GOES-R AOD for Hourly PMEnvironmental science & technology · 2024Article
- Ensemble-based classification approach for PM2.5 concentration forecasting using meteorological data.Frontiers in big data · 2023Article
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Authors and funding
4 authors.
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Abstract
Although many machine learning methods have been widely used to predict PM2.5 concentrations, these single or hybrid methods still have some shortcomings. This study integrated the advantages of convolutional neural network (CNN) feature extraction and the regression ability of random forest (RF) to propose a novel CNN-RF ensemble framework for PM2.5 concentration modeling. The observational data from 13 monitoring stations in Kaohsiung in 2021 were selected for model training and testing. First, CNN was implemented to extract key meteorological and pollution data. Subsequently, the RF algorithm was employed to train the model with five input factors, namely the extracted features from the CNN and spatiotemporal factors, including the day of the year, the hour of the day, latitude, and longitude. Independent observations from two stations were used to evaluate the models. The findings demonstrated that the proposed CNN-RF model had better modeling capability compared with the independent CNN and RF models: the average improvements in root mean square error (RMSE) and mean absolute error (MAE) ranged from 8.10% to 11.11%, respectively. In addition, the proposed CNN-RF hybrid model has fewer excess residuals at thresholds of 10 μg/m
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