增强的联合混合深度神经网络可解释的人工智能模型1小时前进太阳紫外线指数预测
Salvin S Prasad1, Ravinesh C Deo1, Sancho Salcedo-Sanz2
1School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia.
Computer methods and programs in biomedicine
|August 13, 2023
概括
这项研究开发了一个人工智能驱动的紫外线指数预测工具,为高暴露区域提供实时警报. 该系统准确预测紫外线水平,有助于管理与太阳紫外线 (UV) 辐射相关的风险.
科学领域:
- 环境科学 环境科学
- 人工智能的人工智能
- 公共卫生 公共卫生
背景情况:
- 太阳紫外线 (UV) 辐射暴露是皮肤癌和眼睛疾病的重要危险因素.
- 有效管理紫外线风险需要用户友好的,便携式的实时预警系统,特别是可穿戴设备.
- 澳大利亚的热点地区经历了高紫外线暴露,需要有针对性的风险管理策略.
研究的目的:
- 设计一个以人工智能为灵感的早期预警工具,用于短期紫外线指数 (UVI) 预测.
- 整合卫星衍生和地面预测器,以提高澳大利亚热点的紫外线预测准确度.
- 使用可解释的人工智能 (XAI) 提高预测工具的可靠性.
主要方法:
- 开发了一个增强的联合混合解释深度神经网络 (EJH-X-DNN) 模型.
- 使用贝叶斯优化进行了特征选择和超参数调整.
- 模型性能与基准模型进行评估,并使用LIME,SHAP和PFI框架进行解释.
主要成果:
- EJH-X-DNN模型在预测多个澳大利亚热点的每小时UVI方面表现出卓越的性能.
- 高相关系数 (例如,艾利斯斯普林斯的0.960) 已经实现了UVI预测.
- 确定的关键影响特征包括先前的紫外线,太阳顶点角度,臭氧水平,云层和降水.
结论:
- 开发的UVI预测系统为实时紫外线警报提供了显著的好处.
- 该系统有助于减轻皮肤和眼睛健康并发症的风险,可能降低医疗保健成本.
- 该工具可以为户外暴露政策和个人紫外线风险管理提供信息.
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