探索热带气旋对区域空气质量的影响,使用多模式深度学习技术
Muhammad Waqar Younis1, Saritha2, Bhavya Kallapu3
1Department of Computer Science, Aberystwyth University, Penglais, Aberystwyth SY23 3DB, UK.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
热带气旋在事件发生之前,期间和之后显著影响空气质量. 这项研究使用深度学习来预测空气质量变化,帮助气候适应和公共卫生战略.
科学领域:
- 大气科学 大气科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 热带气旋 (TC) 是具有重大环境影响的严重天气事件.
- 了解TC对空气质量的影响对于公共卫生和环境管理至关重要.
研究的目的:
- 调查热带气旋在空气质量指数 (AQI) 变化中的微妙作用.
- 分析TC之前,期间和之后的空气质量变化.
- 提供与TCs相关的AQI波动的预测洞察力.
主要方法:
- 采用多式联运数据,包括气象数据和卫星观测.
- 使用深度学习模型 (ConvLSTM,CNN,Real-ESRGAN) 与回归模型相结合.
- 应用卷积神经网络 (CNN) 用于TC分类和额外树回归器 (ETR) 用于AQI预测.
主要成果:
- 使用CNN进行TC分类,获得了92.02%的准确性.
- 获得了AQI预测的83.33%的R2得分,使用ETR.
- 发现TC特征和AQI之间的复杂模式和非线性相互依赖.
结论:
- 电信在区域和全球空气质量方面发挥着复杂的作用.
- 结果提高了对TC-空气质量相互作用的理解,为决策者和研究人员提供了信息.
- 突出了与气候适应和气旋后城市更新相关的公共卫生问题.
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