Enhanced joint hybrid deep neural network explainable artificial intelligence model for 1-hr ahead solar ultraviolet index prediction.

Journal: Computer methods and programs in biomedicine
Published Date: Aug 5, 2023

Abstract

BACKGROUND AND OBJECTIVE: Exposure to solar ultraviolet (UV) radiation can cause malignant keratinocyte cancer and eye disease. Developing a user-friendly, portable, real-time solar UV alert system especially or wearable electronic mobile devices can help reduce the exposure to UV as a key measure for personal and occupational management of the UV risks. This research aims to design artificial intelligence-inspired early warning tool tailored for short-term forecasting of UV index (UVI) integrating satellite-derived and ground-based predictors for Australian hotspots receiving high UV exposures. The study further improves the trustworthiness of the newly designed tool using an explainable artificial intelligence approach.

Authors

  • Salvin S Prasad
    School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia. Electronic address: salvin.prasad@usq.edu.au.
  • Ravinesh C Deo
    School of Agricultural Computational and Environmental Sciences, International Centre of Applied Climate Science (ICACS), University of Southern Queensland, Springfield, QLD, 4300, Australia. ravinesh.deo@usq.edu.au.
  • Sancho Salcedo-Sanz
    School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia; Department of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, 28805, Madrid, Spain. Electronic address: sancho.salcedo@uah.es.
  • Nathan J Downs
    School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia. Electronic address: nathan.downs@usq.edu.au.
  • David Casillas-Pérez
    Department of Signal Processing and Communications, Universidad Rey Juan Carlos, Fuenlabrada, 28942, Madrid, Spain. Electronic address: david.casillas@urjc.es.
  • Alfio V Parisi
    School of Agricultural, Computational and Environmental Sciences, International Centre for Applied Climate Sciences (ICACS), Institute of Agriculture and Environment (IAg&E), University of Southern Queensland, QLD 4300, Australia.

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