A measurement-based framework integrating machine learning and morphological dynamics for outdoor thermal regulation.

IF 3 3区 地球科学 Q2 BIOPHYSICS
Niloufar Alinasab, Negar Mohammadzadeh, Alireza Karimi, Rahmat Mohammadzadeh, Tamás Gál
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引用次数: 0

Abstract

This study presents a comprehensive investigation into the interplay between machine learning (ML) models, morphological features, and outdoor thermal comfort (OTC) across three key indices: Universal Thermal Climate Index (UTCI), Physiological Equivalent Temperature (PET), and Predicted Mean Vote (PMV). Based on a comprehensive field measurement for 173 urban canyons, proper dataset for summer outdoor thermal condition was provided. Concurrently, six distinct ML models were evaluated and optimized using Bayesian optimization (BO) technique, considering performance indicators like weighted accuracy, F1-Score, precision, and recall. Notable trends emerged, with the CatBoost Classifier demonstrating superior performance in UTCI prediction, the Random Forest classifier excelling in PET estimation, and the XGBoost Classifier achieving optimal PMV prediction. Furthermore, the study delved into the influence of morphological features on OTC, prioritizing factors using SHAP values. Results consistently identified 90-degree orientation, street width, and 180-degree orientation as pivotal factors influencing OTC, with varying degrees of sensitivity across different classifications of thermal stress. Analysis of binary SHAP values unveiled intricate relationships between urban features and OTC indices, emphasizing the critical influence of street orientation on regulating outdoor thermal environments for UTCI and PET scenarios. Surprisingly, street width emerged as the foremost influential factor within the PMV index, challenging established trends and highlighting the complexity of thermal comfort modeling. Additionally, current research delineates the multifaceted impact of street width on microclimate dynamics, enriching our understanding of urban thermal dynamics and emphasizing its role in mitigating thermal stress within urban environments.

一个基于测量的框架,集成了机器学习和形态动力学,用于室外热调节。
本研究通过三个关键指标(通用热气候指数(UTCI)、生理等效温度(PET)和预测平均投票(PMV)),对机器学习(ML)模型、形态特征和室外热舒适(OTC)之间的相互作用进行了全面调查。通过对173个城市峡谷的野外综合测量,提供了适合夏季室外热状况的数据集。同时,使用贝叶斯优化(BO)技术对6种不同的ML模型进行了评估和优化,考虑了加权准确率、F1-Score、精度和召回率等性能指标。值得注意的趋势出现了,CatBoost分类器在UTCI预测方面表现出色,随机森林分类器在PET估计方面表现出色,XGBoost分类器实现了最佳PMV预测。此外,该研究还深入研究了形态特征对OTC的影响,并使用SHAP值对因素进行了排序。结果一致认为90度朝向、街道宽度和180度朝向是影响OTC的关键因素,不同热应力分类的敏感性程度不同。二元SHAP值的分析揭示了城市特征与OTC指数之间的复杂关系,强调了街道朝向对UTCI和PET场景下室外热环境调节的关键影响。令人惊讶的是,街道宽度成为PMV指数中最重要的影响因素,挑战了既定趋势,并突出了热舒适建模的复杂性。此外,目前的研究描绘了街道宽度对微气候动力学的多方面影响,丰富了我们对城市热动力学的理解,并强调了其在城市环境中缓解热应力的作用。
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来源期刊
CiteScore
6.40
自引率
9.40%
发文量
183
审稿时长
1 months
期刊介绍: The Journal publishes original research papers, review articles and short communications on studies examining the interactions between living organisms and factors of the natural and artificial atmospheric environment. Living organisms extend from single cell organisms, to plants and animals, including humans. The atmospheric environment includes climate and weather, electromagnetic radiation, and chemical and biological pollutants. The journal embraces basic and applied research and practical aspects such as living conditions, agriculture, forestry, and health. The journal is published for the International Society of Biometeorology, and most membership categories include a subscription to the Journal.
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