Big Data Solutions for Forecasting Air Pollution

A tétel rövid nézete
dc.contributor.advisorUjvári, Balázs
dc.contributor.authorIbraeva, Zafira
dc.contributor.departmentDE--Informatikai Kar
dc.date.accessioned2025-06-26T21:05:30Z
dc.date.available2025-06-26T21:05:30Z
dc.date.created2025-04-13
dc.description.abstractAir pollution is an alarming concern for all nations due to its serious impacts on human health and the environment. Accurate air quality prediction is significant for taking the required preventive measures, especially for individuals who are prone to the risk factors that result from unhealthy air quality. Until now, several approaches have been developed to forecast the Air Quality Index (AQI), such as the overview of machine learning-based techniques to quantify the concentration of chemicals in the air that result in the air quality index. In this study, we developed several machine learning models, including tree-based algorithms, deep learning sequence models, and traditional approaches, to predict PM10 concentrations. The performance of these models using key evaluation metrics to determine their effectiveness in forecasting air quality was compared and analyzed. Additionally, we built an interactive web application that showcases the models' training and testing results, with visualizations to better interpret predictions and model performance.
dc.description.courseProgramtervező informatikus
dc.description.degreeBSc/BA
dc.format.extent54
dc.identifier.urihttps://hdl.handle.net/2437/394795
dc.language.isoen
dc.rights.infoHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectMachine Learning
dc.subjectBig Data
dc.subjectAir pollution
dc.subjectLSTM
dc.subjectEnsemble model
dc.subjectPM10
dc.subject.dspaceInformatics::Computer Science
dc.titleBig Data Solutions for Forecasting Air Pollution
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