MLP Neural Network Based on PCA and K-means Clustering for PM2.5 Forecasting

Air pollution poses a significant environmental challenge, adversely affecting the health of millions worldwide. Consequently, accurate prediction of pollutant levels has become increasingly crucial to prevent and mitigate the negative impacts of air pollution. This research introduces a Python-based artificial network neural algorithm for predicting PM2.5 levels in Medellín, Colombia, leveraging meteorological and emission data. The model utilizes a Multilayer Perceptron Neural Network, incorporating principal component analysis (PCA) and K-means clustering to determine the optimal number of hidden layers and neurons. Additionally, trend and correlation analyses were conducted to identify the most relevant predictors by examining the relationship between available variables and the target variable (PM2.5). Model performance is assessed using Mean Square Error and Mean Absolute Error.