Estimating the outdoor environment of workers’ villages in East China using machine learning

Zhang, Xingzhao; Yang, Luqiao; Luo, Ruizhe; Wu, Hsin‐Yu; Xu, Jiaqi; Huang, Chenyu; Ruan, Yingjun; Zheng, Xiangyi; Yao, Jiawei

doi:10.1016/j.buildenv.2022.109738

Cited by 6 publications

(2 citation statements)

References 39 publications

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“…Utilizing Computational Algorithms to Calculate Outdoor Thermal Comfort Finally, outdoor thermal comfort is also assessed in conjunction with computer algorithms. For example, Bajsanski et al utilize automatic algorithms with UTCI to assess and improve outdoor thermal comfort under non-steady-state conditions in urban areas [268]; Zhang et al employ machine learning algorithms with UTCI to evaluate the outdoor thermal environment in older neighborhoods [269]; Lai et al use a genetic algorithm with UTCI as the outdoor thermal evaluation index, determining the shape, size, and placement of buildings in the early stages of design to reduce overall thermal stress in outdoor spaces [270]. Machine learning (ML) demonstrates stronger performance in identifying nonlinear and non-standard relationships between independent and dependent variables.…”

Section: Human Health Assessment and Prediction Based On Outdoor Ther...mentioning

confidence: 99%

A Comparative Analysis of Outdoor Thermal Comfort Indicators Applied in China and Other Countries

Tao,

Zhu,

et al. 2023

Sustainability

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Outdoor thermal comfort is an important criterion for evaluating the quality of outdoor activity environments and is also a significant indicator for assessing sustainable building design. Over the past century, more than 165 indoor and outdoor thermal comfort indexes have been developed to define human thermal comfort conditions under various circumstances and to quantify indoor and outdoor thermal environmental conditions. However, in the process of outdoor thermal comfort indicators becoming widely used worldwide, it remains a pressing research issue to compare the current state of application in China and other countries, identify the key areas of application for both sides, and outline the trends in outdoor thermal comfort index application. This study analyzed 346 articles on outdoor thermal comfort indicators. Employing bibliometric methods, we outline the general landscape of outdoor thermal comfort index applications in China and other countries. Additionally, we utilize comparative analysis to uncover similarities and differences in the research focus on outdoor thermal comfort. The research findings indicate the following: (1) Compared to China, other countries started outdoor thermal comfort index application research earlier. Their papers have higher average citation counts and engage in close academic collaborations. However, the quantity of published papers is fewer than in China. (2) The top five frequently used indexes in both China and other countries are PET (including mPET), UTCI, PMV, SET* (including OUT_SET*), and THI (including DI). China tends to use PET and UTCI more frequently than other countries. (3) The potential future directions for outdoor thermal comfort index applications in both China and other countries include: “monitoring and controlling regional outdoor thermal comfort at the temporal and spatial scales”, “multi-factors coupling effects on outdoor thermal comfort”, “human health assessment and prediction based on outdoor thermal comfort”, and “utilizing computational algorithms to calculate outdoor thermal comfort”. This study can serve as a reference for researchers and designers in the industry, contributing to the creation of sustainable outdoor environments.

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Section: Human Health Assessment and Prediction Based On Outdoor Ther...mentioning

confidence: 99%

A Comparative Analysis of Outdoor Thermal Comfort Indicators Applied in China and Other Countries

Tao,

Zhu,

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…Zhang et al used machine learning model to predict environmental indicators via morphological indicators. They compared seven machine learning algorithms for modelling the nonlinear relationship between the building morphology and the outdoor environments of 150 workers' villages in Shanghai [ 13 ]. Zhao et al employed a deep learning simulation method to explore the effects of land use types and density on the spatial distribution of PM2.5 pollutants in the city of Wuhan [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Data-mining framework for in-depth quantitative study of influences on low-wind-velocity area from morphological parameters of cuboid-form buildings

Guo,

Song,

Chu

et al. 2024

Heliyon

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Enhancing Outdoor Comfort: A Machine Learning Framework for Predicting Comfort Level

Alndiwee,

V. K.,

Mathew

et al. 2024

Communications in Computer and Information Science

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Estimating the outdoor environment of workers’ villages in East China using machine learning

Cited by 6 publications

References 39 publications

A Comparative Analysis of Outdoor Thermal Comfort Indicators Applied in China and Other Countries

A Comparative Analysis of Outdoor Thermal Comfort Indicators Applied in China and Other Countries

Data-mining framework for in-depth quantitative study of influences on low-wind-velocity area from morphological parameters of cuboid-form buildings

Enhancing Outdoor Comfort: A Machine Learning Framework for Predicting Comfort Level

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