Enhancing risk communication and environmental crisis management through satellite imagery and AI for air quality index estimation

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dc.contributor.authorKulsawasd Jitkajornwanich
dc.contributor.authorNattadet Vijaranakul
dc.contributor.authorSaichon Jaiyen
dc.contributor.authorPanu Srestasathiern
dc.contributor.authorSiam Lawawirojwong
dc.date.accessioned2026-05-08T19:16:27Z
dc.date.issued2024-2-11
dc.description.abstract> 0.7). We also introduced and tested an additional factor, DOY (day of year), and incorporated it into our model. Additional experiments with similar approaches are also performed and compared. And, the results also show that our hybrid model outperform them. Keywords: climate change, air pollution, air quality assessment, air quality index, AQI, machine learning, AI, Landsat 8, satellite imagery analysis, environmental data analysis, natural disaster monitoring and management, crisis and disaster management and communication.
dc.identifier.doi10.1016/j.mex.2024.102611
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/15513
dc.publisherMethodsX
dc.subjectAir Quality Monitoring and Forecasting
dc.subjectAir Quality and Health Impacts
dc.subjectCOVID-19 impact on air quality
dc.titleEnhancing risk communication and environmental crisis management through satellite imagery and AI for air quality index estimation
dc.typeArticle

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