EMD-based ultraviolet radiation prediction for sport events recommendation with environmental constraint

• 发表在: INFORMATION SCIENCES
• Main Authors: Liu, Ping; Song, Yazhou; Hou, Junjie; Xu, Yanwei
• 格式: 文件
• 语言: English
• 出版: ELSEVIER SCIENCE INC 2025
• 主题: Computer Science
• 在线阅读: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001371914000015
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2024.121592

With the rising awareness of health and wellness, accurate ultraviolet (UV) radiation forecasts have become crucial for planning and conducting outdoor activities safely, particularly in the context of global sporting events arrangement and recommendation with definite constraint on environmental conditions. The dynamic nature of UV exposure, influenced by factors such as solar zenith angles, cloud cover, and atmospheric conditions, makes accurate UV radiation data forecasting difficult and challenging. To cope with these challenges, we present an innovative approach for predicting the UV radiation levels of a certain region during a certain time period using Empirical Mode Decomposition (EMD), a robust method for analyzing non-linear and non-stationary data. Our model is specifically designed for urban areas, where outdoor events are common, and integrates meteorological data with historical UV radiation records from specific geographic regions and time periods. The EMD-based model offers precise predictions of UV levels, essential for event organizers and city planners to make informed decisions regarding the scheduling, relocation and recommendation of events to minimize health risks associated with UV exposure. At last, the effectiveness of this model is validated through various experiments across different spatial and temporal contexts based on the Urban-Air dataset (recording 2,891,393 Air Quality Index data that cover four major Chinese cities), demonstrating its adaptability and accuracy under diverse environmental conditions.