URSUS_LST: URban SUStainability intelligent system for predicting the impact of urban green infrastructure on land surface temperatures

被引：0

作者：

Rodriguez-Gomez, Francisco ^{[1
]}

del Campo-avila, Jose ^{[1
]}

Perez-Urrestarazu, Luis ^{[2
]}

Lopez-Rodriguez, Domingo ^{[3
]}

机构：

[1] Univ Malaga, Dept Lenguajes & Ciencias Comp, Andalucia Tech, Campus Teatinos, Malaga 29071, Spain

[2] Univ Seville, Andalucia Tech, Urban Greening & Biosyst Engn Res Grp, Area Agroforestry Engn, Ctra Utrera Km 1, Seville 41013, Spain

[3] Univ Malaga, Dept Matemat Aplicada, Andalucia Tech, Campus Teatinos, Malaga 29071, Spain

来源：

ENVIRONMENTAL MODELLING & SOFTWARE | 2025年 / 186卷

关键词：

Expert system; Urban greening; Urban heat island; Regression models; Open-source; HEAT-ISLAND; MODELS;

D O I：

10.1016/j.envsoft.2025.106364

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Mitigating Urban Heat Island (UHI) effects has become a challenge to improve urban sustainability. The simulation tool URSUS_LST has been developed to allow urban planners to estimate how the addition of different green infrastructure elements would affect temperature. To achieve this, anew methodology was defined based on data mining, geospatial image processing and the knowledge of experts in the domain that predicts the Land Surface Temperature (LST) of any location within a city. It consists of a first data mining phase in which the real LST and the different urban elements of the nearby environment are considered: buildings, vegetation and water bodies. In a second phase, different regression models are induced to predict LST. Additionally, considering the most accurate models, the relevant attributes and their relationships are identified. Areal application of the tool in the city of Malaga (Spain) has been used as an example of its usefulness.

引用

页数：11

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