Using Artificial Intelligence for Predicting Universal Thermal Climate Index Based on Different Urban Conditions: A Comparative Study of Machine Learning Models DOI
Omid Veisi, Alireza Attarhay Tehrani,

Beheshteh Gharaei

et al.

Published: Jan. 1, 2024

Our research aims to investigate using Artificial Intelligence (AI) methods forecast the Universal Thermal Climate Index (UTCI) in different metropolitan environments. We used several AI models, such as Neural Networks (ANNs), Random Forests (RF), and Gradient Boosting Regressors (GBR), examine data from many cities throughout globe. objective was gain insights into influence of urban architecture on thermal comfort. The emphasizes strong associations between design factors building density, green space ratio, UTCI results, showcasing potential planning climate adaptation. This study focuses two main challenges: computing requirements algorithms limits available imposes. accessible limited a certain set locations rows. Despite these challenges, ANN model achieved notable level precision (MSE=0.008 R2 Score 97), thereby robustness artificial intelligence environmental modeling. To summarize, incorporating procedures may greatly boost our capacity promote comfort settings, therefore contributing development more sustainable habitable cities.

Language: Английский

A measurement-based framework integrating machine learning and morphological dynamics for outdoor thermal regulation DOI Creative Commons
Niloufar Alinasab,

Negar Mohammadzadeh,

Alireza Karimi

et al.

International Journal of Biometeorology, Journal Year: 2025, Volume and Issue: unknown

Published: April 21, 2025

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

Language: Английский

Citations

0
Comparative analysis of thermal and visual comfort perceptions of outdoor military training of University students DOI
Bin Yang, Lei Bai, Zhe Li

et al.

Urban Climate, Journal Year: 2025, Volume and Issue: 61, P. 102418 - 102418

Published: April 22, 2025

Language: Английский

Citations

0
Unveiling Drivers of Zone-Specific Air Quality Predictions Using Explainable Ai: Shapley Additive Explanations-Based Insights Across Formal and Informal End-of-Life Vehicle Recycling Zones with a Green Zone Benchmark DOI
Altaf Hossain Molla, Zambri Harun,

Demiral Akbar

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Leveraging urban AI for high-resolution urban heat mapping: Towards climate resilient cities DOI
Abdulrazzaq Shaamala,

Niklas Tilly,

Tan Yiğitcanlar

et al.

Environment and Planning B Urban Analytics and City Science, Journal Year: 2025, Volume and Issue: unknown

Published: April 24, 2025

Urban heat island (UHI) effects are increasingly recognised as a significant challenge arising from urbanisation, leading to elevated temperatures within urban areas that pose risks public health and undermine the sustainability of cities. Effective UHI management requires high-resolution timely mapping temperature patterns guide interventions. Traditional methods for often lack spatial accuracy efficiency necessary detailed analysis, especially in complex environments. This study integrates artificial intelligence (Urban AI) by presenting U-Net model tailored metropolitan area Adelaide, South Australia. Trained on thermal data Australian Government Data Directory, captures pixel-level variations across diverse landscapes, including densely built areas, suburban zones, green spaces. Achieving low Mean Squared Error (MSE) 0.0029 processing each map less than 30 seconds, demonstrates exceptional computational efficiency. The model, an AI agent, offers scalable tool supporting real-time assessments facilitating targeted mitigation efforts. By bridging gap between advanced geospatial modelling practical planning, it enables data-driven decisions enhance climate resilience, optimise infrastructure, improve rapidly urbanising regions. approach highlights transformative potential addressing challenges, delivering precise actionable insights support sustainable climate-adaptive

Language: Английский

Citations

0
Research on Climate Response Strategies for Traditional Dwellings Based on Shapley Additive Explanations and Machine Learning DOI
Xinyi Zhang, Gongyu Hou, Dandan Wang

et al.

Published: Jan. 1, 2025

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

Language: Английский

Citations

0
Influence of Urban Commercial Street Interface Morphology on Surrounding Wind Environment and Thermal Comfort DOI Creative Commons
Yijie Zhang, Bin Huang

Atmosphere, Journal Year: 2025, Volume and Issue: 16(1), P. 53 - 53

Published: Jan. 7, 2025

In recent climate-adaptive design strategies, there has been a growing interest in creating healthy and comfortable urban microclimates. However, not enough attention paid to the influence of street interface morphology order better understand wind–thermal conditions various commercial streets within city create sustainable built environment. This research summarizes categorizes according their functions types attributes then abstracts ideal models three typical explore effects changes specific morphological parameters on environments. Firstly, this study selects out that affect morphology. Then, it uses numerical simulation software PHOENICS2019 simulate investigate wind environment thermal comfort. The results show (1) neighborhood-commercial streets, reducing void ratio variance height fluctuations can enhance average speed while temperature improving comfort; (2) business-office value is negatively correlated with comfort, aspect are positively correlated; (3) comprehensive-commercial decrease will reduce its increase temperature, thus weakening comfort pedestrians. contrast, as well do significantly These conclusions from provide theoretical basis methodological reference for creation safer, resilient

Language: Английский

Citations

0
One-point-reference-based approach for multi-indoor microclimate prediction based on dynamic-environmental factors DOI
Mallika Kliangkhlao,

Panachat Aiamnam,

Kasidit Boonchai

et al.

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 111945 - 111945

Published: Jan. 1, 2025

Language: Английский

Citations

0
Local thermal sensation model with local skin temperature and local skin heat flux in a personalized heating microenvironment DOI
Guoqing Yu, Renfu Lu, Xing Lv

et al.

Energy and Buildings, Journal Year: 2025, Volume and Issue: unknown, P. 115429 - 115429

Published: Feb. 1, 2025

Language: Английский

Citations

0
Machine learning-based prediction of thermal comfort: exploring building types, climate, ventilation strategies, and seasonal variations DOI
Ali Berkay Avcı

Building Research & Information, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 18

Published: Feb. 15, 2025

Language: Английский

Citations

0
How to realize large-scale outdoor thermal comfort studies? A systematic review based on OTC characterization, methods and research trends DOI Creative Commons
Yuan Li,

Wenyi Fei,

Mengsheng Yang

et al.

Frontiers in Sustainable Cities, Journal Year: 2025, Volume and Issue: 7

Published: March 21, 2025

Introduction With increasing urbanization, the frequency of extreme weather events, and intensification urban heat island (UHI) phenomenon, there is a growing concern about outdoor thermal comfort (OTC) in rural spaces. However, previous OTC studies have been dominated by empirical case regional sample points lacked systematic large-scale exploration within certain region. Methods This study used preferred reporting items for reviews meta-analyses (PRISMA) method bibliometric tools to statisticians sources, keywords, content highly cited papers studies. Results Based on quantitative results, this sorts organizes research from characterization, methods, trends, summarizes following results: (1) Universal climate index (UTCI) relatively suitable research; (2) The combination subjectivity objectivity with application Artificial Intelligence (AI) current cutting-edge OTC; (3) Local zone (LCZ) classification system has potential be future potential. Discussion collated results studies, proposes framework provide necessary theoretical support practical guidance planning construction, which will help optimize environment improve quality life residents.

Language: Английский

Citations

0