Artificial intelligence to classify the cooling effect of tree-shade in buildings’ façade: a case study in Brazil

IF 2.8 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Thaís Rodrigues Ibiapino, Irenilza de Alencar Nääs
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Abstract

Urban heat islands, exacerbated by climate change, have become a pressing issue as summer temperatures rise. This study uses data mining techniques to classify the thermal impact of tree shade on building façades in the urban area of a tropical city. Our objective was to develop models to assist stakeholders and policymakers in forecasting the thermal impact of solar orientation and tree shade on building façades in the urban areas of a tropical city. Minimum and maximum infrared surface temperatures were registered in health clinics’ façades in Teresina, Brazil. Random forest methodology was applied to develop classifying models. This technique, known for its robust classification and prediction of categorical variables, offers a significant advantage over other modeling methods. Key input variables included façade infrared surface temperature, solar orientation, environmental temperature, relative humidity, and the extent of tree shade. Critical attributes were identified as solar orientation (North, South, East, and West), tree shade, and façade temperature (maximum and minimum). Two tree-ensemble models were selected for an accuracy rate of 88% and Kappa (κ) = 0.86. The models indicate that tree-ensemble methods can accurately classify and predict the thermal impact of tree shade on building façades. Additionally, the method effectively identified and ranked the factors influencing thermal impact, providing users with reliable predictive capabilities.

Abstract Image

人工智能对建筑物外墙树荫的降温效果进行分类:巴西案例研究
随着夏季气温的升高,因气候变化而加剧的城市热岛已成为一个紧迫的问题。本研究采用数据挖掘技术,对热带城市市区树荫对建筑物外墙的热影响进行分类。我们的目标是建立模型,帮助利益相关者和政策制定者预测太阳方位和树荫对热带城市市区建筑外墙的热影响。我们对巴西特雷西纳市医疗诊所外墙的最低和最高红外表面温度进行了登记。随机森林方法被用于开发分类模型。该技术以对分类变量进行稳健分类和预测而著称,与其他建模方法相比具有显著优势。关键输入变量包括外墙红外表面温度、太阳方位、环境温度、相对湿度和树荫范围。关键属性包括太阳方位(北、南、东和西)、树荫和外墙温度(最高和最低)。选定的两个树状集合模型的准确率为 88%,Kappa (κ) = 0.86。这些模型表明,树形集合方法可以准确地分类和预测树荫对建筑物外墙的热影响。此外,该方法还有效地识别了影响热影响的因素并对其进行了排序,为用户提供了可靠的预测能力。
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来源期刊
Theoretical and Applied Climatology
Theoretical and Applied Climatology 地学-气象与大气科学
CiteScore
6.00
自引率
11.80%
发文量
376
审稿时长
4.3 months
期刊介绍: Theoretical and Applied Climatology covers the following topics: - climate modeling, climatic changes and climate forecasting, micro- to mesoclimate, applied meteorology as in agro- and forestmeteorology, biometeorology, building meteorology and atmospheric radiation problems as they relate to the biosphere - effects of anthropogenic and natural aerosols or gaseous trace constituents - hardware and software elements of meteorological measurements, including techniques of remote sensing
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