Predicting Surface Urban Heat Island in Meihekou City, China: A Combination Method of Monte Carlo and Random Forest

2021 ◽  
Vol 31 (4) ◽  
pp. 659-670
Author(s):  
Yao Zhang ◽  
Jiafu Liu ◽  
Zhuyun Wen
Keyword(s):  
Monte Carlo ◽  
Random Forest ◽  
Urban Heat Island ◽  
Combination Method ◽  
Heat Island ◽  
Urban Heat ◽  
Surface Urban Heat Island

The impact of COVID-19 lockdowns on surface urban heat island changes and air-quality improvements across 21 major cities in the Middle East

2021 ◽  
pp. 117802
Author(s):  
Ahmed M. El Kenawy ◽  
Juan I. Lopez-Moreno ◽  
Matthew F. McCabe ◽  
Fernando Domínguez-Castro ◽  
Dhais Peña-Angulo ◽  
...  
Keyword(s):  
Air Quality ◽  
Middle East ◽  
Urban Heat Island ◽  
Heat Island ◽  
Quality Improvements ◽  
Urban Heat ◽  
Surface Urban Heat Island ◽  
The Impact

Quantifying the response of surface urban heat island to urban greening in global north megacities

2021 ◽  
pp. 149553
Author(s):  
Fengqi Cui ◽  
Rafiq Hamdi ◽  
Xiuliang Yuan ◽  
Huili He ◽  
Tao Yang ◽  
...  
Keyword(s):  
Urban Heat Island ◽  
Heat Island ◽  
Urban Greening ◽  
Global North ◽  
Urban Heat ◽  
Surface Urban Heat Island

scholarly journals Disproportionate exposure to urban heat island intensity across major US cities

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Angel Hsu ◽  
Glenn Sheriff ◽  
Tirthankar Chakraborty ◽  
Diego Manya
Keyword(s):  
Urban Heat Island ◽  
Census Tract ◽  
Demographic Data ◽  
Heat Exposure ◽  
Poverty Line ◽  
Built Environments ◽  
Heat Island ◽  
Urban Heat ◽  
Surface Urban Heat Island ◽  
Us Cities

AbstractUrban heat stress poses a major risk to public health. Case studies of individual cities suggest that heat exposure, like other environmental stressors, may be unequally distributed across income groups. There is little evidence, however, as to whether such disparities are pervasive. We combine surface urban heat island (SUHI) data, a proxy for isolating the urban contribution to additional heat exposure in built environments, with census tract-level demographic data to answer these questions for summer days, when heat exposure is likely to be at a maximum. We find that the average person of color lives in a census tract with higher SUHI intensity than non-Hispanic whites in all but 6 of the 175 largest urbanized areas in the continental United States. A similar pattern emerges for people living in households below the poverty line relative to those at more than two times the poverty line.

Statistical analysis of surface urban heat island intensity variations: A case study of Babol city, Iran

2018 ◽  
Vol 56 (4) ◽  
pp. 576-604 ◽  
Author(s):  
Qihao Weng ◽  
Mohammad Karimi Firozjaei ◽  
Amir Sedighi ◽  
Majid Kiavarz ◽  
Seyed Kazem Alavipanah
Keyword(s):  
Statistical Analysis ◽  
Urban Heat Island ◽  
Urban Heat Island Intensity ◽  
Heat Island ◽  
Urban Heat ◽  
Surface Urban Heat Island

scholarly journals A High-Resolution Monitoring Approach of Canopy Urban Heat Island using Random Forest Model and Multi-platform Observations

2021 ◽  
Author(s):  
Shihan Chen ◽  
Yuanjian Yang ◽  
Fei Deng ◽  
Yanhao Zhang ◽  
Duanyang Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Anthropogenic Heat ◽  
Heat Island ◽  
Model Framework ◽  
Rapid Urbanization ◽  
Urban Heat ◽  
The City

Abstract. Due to rapid urbanization and intense human activities, the urban heat island (UHI) effect has become a more concerning climatic and environmental issue. A high spatial resolution canopy UHI monitoring method would help better understand the urban thermal environment. Taking the city of Nanjing in China as an example, we propose a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a Random Forest (RF) model. Firstly, the observed environmental parameters [e.g., surface albedo, land use/land cover, impervious surface, and anthropogenic heat flux (AHF)] around densely distributed meteorological stations were extracted from satellite images. These parameters were used as independent variables to construct an RF model for predicting air temperature. The correlation coefficient between the predicted and observed air temperature in the test set was 0.73, and the average root-mean-square error was 0.72 °C. Then, the spatial distribution of CUHII was evaluated at 30-m resolution based on the output of the RF model. We found that wind speed was negatively correlated with CUHII, and wind direction was strongly correlated with the CUHII offset direction. The CUHII reduced with the distance to the city center, due to the de-creasing proportion of built-up areas and reduced AHF in the same direction. The RF model framework developed for real-time monitoring and assessment of high-resolution CUHII provides scientific support for studying the changes and causes of CUHII, as well as the spatial pattern of urban thermal environments.

Surface Urban Heat Island Across 419 Global Big Cities

2011 ◽  
Vol 46 (2) ◽  
pp. 696-703 ◽  
Author(s):  
Shushi Peng ◽  
Shilong Piao ◽  
Philippe Ciais ◽  
Pierre Friedlingstein ◽  
Catherine Ottle ◽  
...  
Keyword(s):  
Urban Heat Island ◽  
Heat Island ◽  
Urban Heat ◽  
Surface Urban Heat Island ◽  
Big Cities
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