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使用机器学习预测高分辨率空气质量:集成大模拟和城市形态数据

Shibao Wang¹, Jeremy McGibbon², Yanxu Zhang¹

¹School of Atmospheric Sciences, Nanjing University, Nanjing, Jiangsu, China.

Environmental pollution (Barking, Essex : 1987)

|January 24, 2024

概括

这项研究引入了一个卷积神经网络 (CNN),用于精确的城市空气质量预测,其性能优于传统方法. 该CNN模型准确预测一氧化碳 (CO) 的分布,有助于城市空气管理.

科学领域:

环境科学环境科学
大气化学大气化学
城市规划城市规划

背景情况:

准确的城市空气污染物预测对于公共卫生和环境管理至关重要.
传统的机器学习模型在复杂的城市环境中难以获得准确性和可解释性.
现有的方法往往缺乏详细的城市空气质量分析所需的空间分辨率.

研究的目的:

开发和验证一种新的卷积神经网络 (CNN) 模型,用于高分辨率地预测一氧化碳 (CO) 度.
将各种各样的城市因素包括建筑高度,地形和排放纳入预测模型.
提高空气质量预测模型的可解释性,揭示关键影响因素.

主要方法:

一个卷积神经网络 (CNN) 模型被开发用于预测南京10米分辨率的CO度.
美国有线电视新闻网 (CNN) 在48个不同的场景中使用并行大模拟 (PALM) 模型模拟的数据进行训练.
模型性能经过严格评估,使用10倍和样本外交叉验证,以及对第二个城市的应用.

主要成果:

与PALM模拟相比,CNN模型表现出强大的一致性和高精度 (R2>0.8,低RMSE).
权重贡献图显示,当地的建筑物和排放显著影响了二氧化碳度,突出了邻里效应.
该模型显示了强大的概括能力,在应用到不同的城市地区时,实现了高预测准确性.

结论:

综合CNN-PALM框架显著提高了城市空气质量预测的准确性和可解释性.
该模型为CO的空间分布提供了有价值的见解,受城市形态和气象因素的影响.
这种方法为城市空气质量管理和政策制定提供了有效的工具.

关键词:

卷积神经网络 (CNN) 是一种神经网络.对交叉验证进行验证.功能地图的特点地图城市空气质量预测预测

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