A HYBRID DEEP LEARNING MODEL FOR FORECASTING PM2.5 AIR POLLUTANT CONCENTRATIONS

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dc.contributor.authorMassad, Asma
dc.date.accessioned2025-01-23T10:40:51Z
dc.date.available2025-01-23T10:40:51Z
dc.date.issued2024-12-18
dc.description.abstractAir quality forecasting is a crucial research field that aids scientists and policymakers in making informed decisions to combat air pollution. Among various pollutants, PM2.5 -particulate matter with a diameter smaller than 2.5 micrometers- poses significant health risks, as it can reach the lower respiratory tract and enter the bloodstream. Accurately forecasting PM2.5 levels is thus essential. Although machine learning-based spatiotemporal forecasting models have advanced, the pursuit for more accurate forecasts continues. The use of hybrid deep learning models for PM2.5 forecasting represents a promising and active area of research, as these models aim to capture complex spatiotemporal dependencies more effectively. We developed a Dynamic Graph Attention Network (DyGAT) to model spatial dependencies effectively. DyGAT leverages engineered edge features, including distance, wind speed, and wind direction, while using attention mechanisms to capture the dynamic nature of these dependencies. DyGAT was then combined with Informer, a Transformer for efficient time-series forecasting, to capture spatial and temporal patterns comprehensively, improving prediction accuracy. Our model was evaluated on a benchmark dataset from Beijing, with 420,768 records over four years. DyGAT-Informer outperformed a version without the DyGAT component and other baseline models. It achieved 50.43 for MAE, 79.9 for RMSE and 28.88% for SMAPE, compared to 51.44 for MAE, 80.83 for RMSE and 30.25% for SMAPE in the next best model. Additionally, we conducted a case study using a dataset from Nablus, Palestine, consisting of 2692 records per station over a two months period. We incorporated geospatial features about nearby pollution sources into the dataset. Due to the insufficient number of records in the Nablus dataset for training the Informer, it was replaced with a sequence-to-sequence Long Short-Term Memory (LSTM) model. DyGAT-LSTM, trained with additional geospatial features about nearby pollution sources, achieved a 2.08% reduction in MAE, 1.17% in RMSE, and 1.96% in SMAPE. This confirms the benefit of incorporating such data. Finally, despite the short distances between stations, DyGAT successfully captured spatial dependencies, where DyGAT-LSTM achieved a reduction of 3.13% in MAE, 1.48% in RMSE, and 3.67% in SMAPE when compared to the LSTM-only model.
dc.identifier.urihttps://hdl.handle.net/20.500.11888/19824
dc.language.isoen
dc.publisherAn-Najah National University
dc.supervisorToma, Anas
dc.supervisorKhader, Abdelhaleem
dc.titleA HYBRID DEEP LEARNING MODEL FOR FORECASTING PM2.5 AIR POLLUTANT CONCENTRATIONS
dc.title.alternativeنموذج تعلم عميق هجين للتنبؤ بتركيزات ملوث الهواء PM2.5
dc.typeThesis
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