SCC-IRG Track 2: A data-driven approach to designing a community-focused indoor heat emergency alert system for vulnerable residents (CommHEAT)

极端高温是致命的，不成比例地影响老年人和低收入社区的居民。未来几年，极端高温和潮湿事件将增加。缺乏空调和城市热岛效应造成危险的室内条件。在低收入地区，高达60%的老旧、劣质房屋缺乏空调。在机器学习（SciML）和基于代理的模型（ABM）中结合居民的行为和建筑特征，将识别居民何时在家中暴露于过热风险，并将其与资源联系起来，以减轻危险状况。利用爱荷华州州立大学、得梅因市、波尔克县、社区组织以及UNI和UTA的合作者之间的合作，该团队将利用经验数据和参与过程开发新型混合社会和物理感知模型，以提高与极端高温相关的室内条件的可预测性。一个以社区为重点的小气候知情的室内热应急警报（CommHEAT）系统将个性化社区热相关的应急管理能力。这将通过改善对家庭室内条件的预测以适应极端高温来提供社会效益。社会-生物物理模型可广泛用于促进气候适应和改善与极端高温相关的公共健康。项目完成后，将为社区提供一个真实的小气候信息热警报框架。结果将支持当地的热健康行动计划，以减少紧急呼叫，热病相关的住院治疗，并从室内热暴露的死亡率。这项研究将通过适应/响应室内极端高温的社会和热物理模型来解决知识差距。数据将把对极端高温的社会/行为反应与物理约束模型相结合，并在空间和时间尺度上制定应对策略。智力价值包括开发新的数据驱动建模，结合验证的ABM和物理约束的SciML模型的建筑特征与建筑物内/附近的人类行为。该项目有助于三项科学进步：（1）基于人类选择描述极端事件期间的人类行为;（2）创建具有实时参数的本地化物理约束室内条件模型;（3）使用可转移框架将模型集成到应用程序中，以预测随着时间的推移的条件。通过与研究区域弱势居民的参与式设计，ABM将成为一个经验有效的社区模型，能够在热警报系统中预测气候情景下对不同应用程序启用的热缓解策略的反应，从而改善健康结果。可转移的SciML将解释将局部条件与建筑热特性联系起来的基础物理学。CommHEAT应用程序将可视化警报缓解情景，以指导多个尺度的适应决策。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Extreme heat is deadly and disproportionately affects the elderly and residents of low-income neighborhoods. Extreme heat and humidity events will increase in coming years. Lack of air-conditioning and the urban heat island effect create dangerous indoor conditions. Up to 60% of older, poorly built homes in low-income areas lack AC. Combining residents’ behavior and building characteristics in machine learning (SciML) and agent-based models (ABM) will identify when residents are exposed to overheating risks in their home and connect them with resources to mitigate dangerous conditions. Leveraging collaboration between Iowa State University, the City of Des Moines, Polk County, community organizations, and collaborators at UNI and UTA, this team will use empirical data and participatory processes to develop novel hybrid social- and physics-aware models to increase predictability of extreme heat-related indoor conditions. A community-focused microclimate-informed indoor heat emergency alert (CommHEAT) system will personalize community heat-related emergency management capacity. This will provide societal benefits via improved prediction of indoor conditions in homes for adaptation to extreme heat. The social-biophysical models are broadly transferable to facilitate climate adaptation and improve public health associated with extreme heat. Project completion will provide communities with a framework for microclimate-informed heat alerts in real time. Outcomes will support local heat health action plans to reduce emergency calls, heat illness-related hospitalizations, and mortality from indoor heat exposure.This research will address knowledge gaps through social and thermal-physical models of adaptation/response to extreme heat indoors. Data will integrate social/behavioral responses to extreme heat with physics-constrained models and develop response strategies across spatial and temporal scales. Intellectual merit includes development of novel data-driven modeling combining validated ABM and physics-constrained SciML models of building features with human behavior within/near buildings. This project contributes to three scientific advances: (1) describing human behavior during extreme events based on human choices; (2) creating localized physics-constrained indoor condition models with real-time parameters; and (3) integrating models in an app using a transferable framework to predict conditions over time. Through participatory design with vulnerable residents in the study area the ABM will be an empirically valid model of the community, enabling prediction of responses to different app-enabled heat mitigation strategies under climate scenarios in a heat alert system that will improve health outcomes. Transferable SciML will account for underlying physics that tie local conditions to building thermal properties. The CommHEAT app will visualize alert mitigation scenarios to guide decision-making at multiple scales for adaptation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.