CyberSEES: Type 1: A Novel Machine Learning Framework for Urban Heat Island Causal Analysis: a Fusion of Observations and Physical Models

城市热岛是一种现象，即城市地区的地表和大气温度高于周围的郊区和农村地区。这些较高的温度导致夏季建筑冷却能源使用量的增加，并可能加剧城市空气污染，人体热舒适和热浪。该项目结合了机器学习、土木与环境工程和地球科学三个领域的专业知识，开发了一个新的框架，该框架将数据驱动的方法和基于物理的气候模拟模型相结合，以更好地了解世界各地城市热岛的物理过程驱动因素。更好地了解城市热岛的主要驱动因素对于确定适当的工程解决方案以缓解城市热岛变暖至关重要。这项研究将通过改变人们参与科学数据分析任务的方式产生广泛的社会影响，并将在计算可持续性方面建立一个更强大的研究体系。提出的框架将在一个发现过程中集成数据驱动的方法和基于物理的模型。它包括三个重要步骤：(i)潜在特征发现，旨在通过深度网络从大规模观测数据中自动推断高级特征表示。这些潜在的特征捕获了观察变量的复杂非线性变换，作为潜在物理过程的隐喻；（ii）潜在特征解释，通过编译的数据集从潜在特征中生成城市热岛驱动因素候选。它提供了对这些潜在特征如何与物理过程相关联的见解；（iii）城市热岛驱动因素识别，设计有效的实验，通过改变模拟模型的观测变量来识别热岛的原因。该项目预计将促进对导致城市热岛的主要物理过程驱动因素的了解。此外，开发的框架具有推进机器学习的潜力，包括特征学习，混杂因素的因果分析和因果实验设计。计算工具的源代码和通过该项目收集的数据集将免费分发给更广泛的研究和教育界。

The urban heat island is a phenomenon whereby urban areas have higher surface and atmospheric temperatures than surrounding suburban and rural regions. These higher temperatures lead to increases in building cooling energy use during summer, and can exacerbate urban air pollution, human thermal comfort, and heat waves. This project unites expertise in three fields---machine learning, civil and environmental engineering, and earth science---to develop a novel framework that integrates data-driven approaches and physics-based climate simulation models to better understand the physical process drivers of urban heat islands around the world. Better understanding the main drivers of urban heat islands in cities is critical for identifying the appropriate engineering solutions for mitigating urban warming from heat islands. This research will have broad societal impact by changing the ways that people participate in scientific data analysis tasks, and will build a stronger body of research in computational sustainability. The proposed framework will integrate data-driven approaches and physically based models in one discovery process. It consists of three important steps: (i) latent feature discovery, which aims to automatically infer high-level feature representations from large scale observational data via deep networks. These latent features capture the complex nonlinear transformation of observed variables as a metaphor of latent physical processes; (ii) latent feature interpretation, which generates candidates of urban heat island drivers from the latent features via a compiled dataset. It provides insights into how these latent features are associated with physical processes; and (iii) urban heat island driver identification, which designs effective experiments to identify the causes of heat islands by varying observed variables from simulation models. The project is expected to advance the knowledge of key physical process drivers causing urban heat islands in cities. In addition, the developed framework has the potential to advance machine learning, including feature learning, causal analysis with confounders, and causal experiment design. The source code for computational tools and the data sets collected through the project will be freely disseminated to the broader research and educational community.