Data-driven Approach to Estimate Urban Heat Island Impacts on Building Energy Consumption

Energy, Journal Year: 2025, Volume and Issue: 316, P. 134508 - 134508

Published: Jan. 11, 2025

Language: Английский

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From Data to Insights: Modeling Urban Land Surface Temperature Using Geospatial Analysis and Interpretable Machine Learning

Sensors, Journal Year: 2025, Volume and Issue: 25(4), P. 1169 - 1169

Published: Feb. 14, 2025

This study introduces an innovative machine learning method to model the spatial variation of land surface temperature (LST) with a focus on urban center Da Nang, Vietnam. Light Gradient Boosting Machine (LightGBM), support vector machine, random forest, and Deep Neural Network are employed establish functional relationships between LST its influencing factors. The approaches trained validated using remote sensing data from 2014, 2019, 2024. Various explanatory variables representing topographical characteristics, as well landscapes, used. Experimental results show that LightGBM outperforms other benchmark methods. In addition, Shapley Additive Explanations utilized clarify impact factors affecting LST. analysis outcomes indicate while importance these changes over time, density greenspace consistently emerge most influential attained R2 values 0.85, 0.92, 0.91 for years 2024, respectively. findings this work can be helpful deeper understanding heat stress dynamics facilitate planning.

Language: Английский

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A review of urban heat island mapping approaches with a special emphasis on the Indian region

Environmental Monitoring and Assessment, Journal Year: 2025, Volume and Issue: 197(4)

Published: March 8, 2025

Language: Английский

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Development of data-driven estimation models of village carbon emissions by built form factors: The study in Huaihe River Basin, China

Building and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 112846 - 112846

Published: March 1, 2025

Language: Английский

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Towards Universal Thermal Climate Index Prediction via machine learning approaches

Renewable and Sustainable Energy Reviews, Journal Year: 2025, Volume and Issue: 217, P. 115680 - 115680

Published: April 11, 2025

Language: Английский

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Quantifying the Effects of Climate Change on the Urban Heat Island Intensity in Luxembourg—Sustainable Adaptation and Mitigation Strategies Through Urban Design

Atmosphere, Journal Year: 2025, Volume and Issue: 16(4), P. 462 - 462

Published: April 16, 2025

Rapid urbanization and climate change intensify the urban heat island effect. This study quantifies UHI impact in Luxembourg’s Pro-Sud region explores sustainable mitigation strategies. In situ mobile measurements, EURO-CORDEX regional projections (RCP4.5), FITNAH-3D model were used considering also future building developments. The results reveal a significant effect, with substantial temperature thermal stress level differences between rural areas. Regional indicate marked intensification under scenarios. simulations show increased levels, especially densely built areas, highlight green infrastructure’s importance mitigating effects. Recommendations for spatial unit-specific measures specifically vegetation, unsealing, optimized design planning are provided. Our research emphasizes urgent need tailored planning, adaptation, strategies to enhance resilience address stress.

Language: Английский

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Leveraging urban AI for high-resolution urban heat mapping: Towards climate resilient cities

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

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A resilient disaster recovery model for Puerto Rico: a qualitative case study

Environmental Hazards, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 21

Published: Aug. 12, 2024

Environmental disruptions cause various damages to cities and communities. Many Puerto Ricans have endured catastrophes, suffered the loss of friends family, resided in temporary shelters, been uncertain when they could return home. Thus, this study aims understand factors that help Rican communities better respond recover from shocks. Through a multiple-case-study design, analyses four identify indicators most significantly contribute resilient disaster recovery. Data is collected via in-person interviews analysed qualitative inductive coding. The results emphasise role internal – social cohesion, community engagement, local leadership external political vulnerability, available resources, participation preparation planning, sustainable development This research deepens our comprehension critical shape recovery efforts within It also aids enhancing their preparedness, coping mechanisms, adaptation strategies offers guidance for urban planners policymakers concentrate on bolster adaptive capabilities resilience Rico.

Language: Английский

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Past, Present, and Future Perspectives on the Integration of AI Into Walkability Assessment Tools: A Systematic Review

Urban Planning, Journal Year: 2024, Volume and Issue: 10

Published: Sept. 18, 2024

This study employs a systematic literature review (PRISMA methodology) to investigate the integration of Artificial Intelligence (AI) in walkability assessments conducted between 2012 and 2022. Analyzing 34 articles exploring data types, factors, AI tools, emphasizes value utilizing diverse datasets, particularly street view images, train supersized models. approach fosters efficient, unbiased offers deep insights into pedestrian environment interactions. Furthermore, tools empower assessment by facilitating mapping, scoring, designing routes, uncovering previously unconsidered factors. The current shift from large-scale spatial analysis (allocentric perspective) ground-level (egocentric physical perceptual features walking introduces subjective lens tools. However, efficacy methods addressing non-visual aspects human perception their applicability across demographics remains debatable. Finally, lack emerging technologies like virtual/augmented reality digital twin leaves significant gap research, inviting further determine enhancing and, general, understanding interaction humans cities.

Language: Английский

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Deep multifractal detrended cross-correlation analysis algorithm for multifractals

Physica A Statistical Mechanics and its Applications, Journal Year: 2024, Volume and Issue: unknown, P. 130105 - 130105

Published: Sept. 1, 2024

Language: Английский

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