AI 中文总结
研究针对热应激指数与人类热感知相关性不明的问题，开发多项式混沌展开（PCE）代理和多层感知器（MLP）分类器两个互补框架，用于热感觉建模，揭示不同气候下两者敏感性结构和性能差异及地理依赖性。
AI 中文摘要
热应激指数旨在量化生理热应激，但其与推断个体热感知的相关性尚不清楚。本研究表明热应激和热感觉常不一致，通过对环境驱动因素的不同全局敏感性模式得以证明。利用热感觉投票调查数据，发现基于应激的指标的主要敏感性与人类报告的热感觉指标不一致。鉴于全球适用的热应激指数众多且缺乏可比的一般热感觉指标，开发了两个互补的数据驱动热感觉建模框架。一是构建多项式混沌展开（PCE）代理，将热感觉表示为气象变量的函数，实现基于方差的有效敏感性分析并明确识别有影响的输入和相互作用；二是开发多层感知器（MLP）分类器，捕捉热感知的非线性和主观性，同时实现高预测准确性。PCE模型提供可物理解释的敏感性以解释热感觉驱动因素，MLP提供适用于复杂环境的灵活预测能力。并在城市和大陆尺度应用这两种建模方法，揭示了不同气候下敏感性结构和性能的系统差异。特别是，发现基于热感觉投票（TSV）的模型对不同地球气候区气象条件变化的敏感性编码了对温度、辐射、湿度和风的不同地理依赖性，且通常与热应激指数不同。
英文摘要
Heat stress indices are designed to quantify physiological thermal stress, but their relevance for inferring the thermal perception of individuals remains unclear. In this study, we show that thermal stress and thermal sensation often diverge, as evidenced by distinct global sensitivity patterns with respect to environmental drivers. Using thermal sensation vote survey data, we demonstrate that the dominant sensitivities of stress-based metrics do not align with those governing reported human thermal sensation. Given the multitude of globally-applicable thermal stress indices and the lack of comparable general thermal sensation metrics, we develop two complementary data-driven modeling frameworks for thermal sensation. First, we construct polynomial chaos expansion (PCE) surrogates to represent thermal sensation as a function of meteorological variables, enabling efficient variance-based sensitivity analysis and explicit identification of influential inputs and interactions. Second, we develop multilayer perceptron (MLP) classifiers that capture the nonlinear and subjective nature of thermal perception, while achieving high predictive accuracy. The PCE models provide physically interpretable sensitivities that can explain the drivers of thermal sensation, while the MLPs offer flexible predictive capability suited to complex environments. We apply both modeling approaches at city- and continent-scales, revealing systematic differences in sensitivity structure and performance across climates. In particular, we find that the sensitivity of TSV-based models to the variability of meteorological conditions across geoclimatic zone encodes distinct dependencies on temperature, radiation, humidity, and wind that vary geographically, and are generally different from those of heat stress indices.
