An RF-CNN pipeline for predicting PM2.5 concentration in Sri Lanka

• The RF-CNN pipeline can serve as a robust and comprehensive method for precisely forecasting the spatial and temporal fluctuations in PM 2.5 concentrations. • RF-CNN pipeline yielded satisfactory outcomes by employing the RF component as an error model. • The RF-CNN pipeline is intended to enhance air quality forecasting and guide policymakers in mitigating air pollution impacts worldwide. Air pollution is a considerable global public health threat, requiring efficient monitoring and forecasting to guide decision-making. This study introduces a cascaded model of enhanced Random Forest with Convolutional Neural Network (RF-CNN) that predicts spatiotemporal fluctuations in PM 2.5 concentrations throughout Sri Lanka. The K-Nearest Neighbors method is employed to impute missing data, and the model utilizes data from 24 low-cost PM 2.5 sensors that are distributed throughout the country. The Convolutional Neural Network (CNN) derives spatial features from four-band PlanetScope satellite images (3m/pixel resolution, 1km 2 spatial coverage), while the Random Forest (RF) component models the relationship between PM 2.5 levels and four meteorological parameters. These features, combined with meteorological, spatial, and temporal inputs, produce the final forecasting results. The dataset comprises 1934 satellite images that were collected between December 2022 and February 2024, with an average PM 2.5 concentration of approximately 15 μg/m 3 . The RF-CNN model exhibited robust performance metrics across a variety of climate zones, including a normalized root mean square error of approximately 32.4%, a mean absolute percentage error of approximately 25.7%, a normalized mean absolute error of approximately 22.8%, a Spearman r of 0.871, and a Pearson r of 0.873. Two metrics: Input Data Quality Score (IDQS) and Testing Data Quality Score (TDQS) were implemented to evaluate the effects of imputation. Performance was minimally impacted by imputation within acceptable ranges, while exceeding limits resulted in increased uncertainty. This research emphasizes the efficacy of the RF-CNN approach, which integrates satellite imagery and low-cost sensor data, as a scalable solution for predicting spatiotemporal PM 2.5 variations. It provides valuable insights for regions that lack extensive monitoring.