Modeling PM_(2.5) Urban Pollution Using Machine Learning and Selected Meteorological Parameters

Cifuentes, F., Gálvez, A., González, C. M., Alzate, M. O., & Aristizábal, B. H. (2021). Hourly Ozone and PM_(2.5) Prediction Using Meteorological Data - Alternatives for Cities with Limited Pollutant Information. Aerosol and Air Quality Research, 21(9), 1-21+ap1-9-016. https://doi.org/10.4209/aaqr.200471
Liu, C. C. (2016). Using Support Vector Machine Method with Spatio-temporal Features to Predict Urban Air Quality [master's thesis, National Taiwan University]. Airiti Library. https://doi.org/10.6342/NTU201601224
Chang, C. L. (2018). Improving the accuracy Rate of PM2.5 Pollution Forecast Using Q-learning training Time-series Model [master's thesis, National Taiwan University]. Airiti Library. https://doi.org/10.6342/NTU201804115
Cao, Y., Zhao, X., Su, D., Cheng, X., & Ren, H. (2023). A Machine-Learning-Based Classification Method for Meteorological Conditions of Ozone Pollution. Aerosol and Air Quality Research, 23(1), 1-17. https://doi.org/10.4209/aaqr.220239
Chiang, Y. L. (2019). On the monitoring and prediction for PM2.5 in urban areas [master's thesis, National Taiwan University]. Airiti Library. https://doi.org/10.6342/NTU201901826