太阳紫外线（UV）辐射暴露可能导致恶性角质细胞癌和眼部疾病。开发一种用户友好、便携、实时的太阳紫外线警报系统，特别是适用于可穿戴式电子移动设备，有助于减少紫外线暴露，作为个人和职业性紫外线风险管理的关键措施。本研究旨在设计一种受人工智能启发的早期预警工具，用于针对澳大利亚高紫外线暴露的热点地区的紫外线指数（UVI）的短期预测，该工具整合了卫星获取的和基于地面的预测因素。该研究还使用可解释的人工智能方法提高了新设计工具的可靠性。 采用增强型联合混合可解释深度神经网络模型（称为EJH-X-DNN），该模型包括特征选择和超参数调整两个阶段，使用贝叶斯优化进行。通过与其他六个竞争基准模型的全面评估，我们进一步验证了EJH-X-DNN模型的可靠性。提出的模型使用局部解释性模型无关解释（LIME）、Shapley可加性解释（SHAP）和置换特征重要性（PFI）等强大的模型无关解释框架进行局部和全局解释。 新提出的模型在预测小时紫外线指数方面表现出色，针对达尔文、艾丽斯泉、汤斯维尔和埃默拉尔德等热点地区，相关系数分别为0.900、0.960、0.897和0.913。根据基于现场的综合局部和全局解释模型结果，先前滞后的UVI和太阳天顶角是影响因素。EJH-X-DNN模型的预测受到臭氧效应、云情况和降水等因素的强烈影响。 UVI预测系统以其卓越性和熟练解释的能力，再次证实了提供实时紫外线警报以减轻皮肤和眼部健康并发症风险、降低医疗费用并为户外暴露政策做出贡献的好处。 版权所有 © 2023 作者。由Elsevier B.V.出版。保留所有权利。

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.An enhanced joint hybrid explainable deep neural network model (called EJH-X-DNN) is constructed involving two phases of feature selection and hyperparameter tuning using Bayesian optimization. A comprehensive assessment of EJH-X- DNN is conducted with six other competing benchmarked models. The proposed model is explained locally and globally using robust model-agnostic explainable artificial intelligence frameworks such as Local Interpretable Model-Agnostic Explanations (LIME), Shapley additive explanations (SHAP), and permutation feature importance (PFI).The newly proposed model outperformed all benchmarked models for forecasting hourly horizons UVI, with correlation coefficients of 0.900, 0.960, 0.897, and 0.913, respectively, for Darwin, Alice Springs, Townsville, and Emerald hotspots. According to the combined local and global explainable model outcomes, the site-based results indicate that antecedent lagged memory of UVI and solar zenith angle are influential features. Predictions made by EJH-X-DNN model are strongly influenced by factors such as ozone effect, cloud conditions, and precipitation.With its superiority and skillful interpretation, the UVI prediction system reaffirms its benefits for providing real-time UV alerts to mitigate risks of skin and eye health complications, reducing healthcare costs and contributing to outdoor exposure policy.Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.