2026-06-02T05:56:55Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/384712026-02-03T10:54:54Zcom_10630_2254col_10630_37953

URSUS_LST: URban SUStainability intelligent system for predicting the impact of urban green infrastructure on land surface temperatures Rodríguez-Gómez, Francisco Del-Campo-Ávila, José Pérez-Urrestarazu, Luis López-Rodríguez, Domingo Soporte lógico libre Urbanismo sostenible 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, a new 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. A real application of the tool in the city of Malaga (Spain) has been used as an example of its usefulness. 2025-04-24T07:19:46Z 2025-04-24T07:19:46Z 2025-04-24T07:19:46Z 2025 journal article Rodríguez-Gómez, F., del Campo-Ávila, J., Pérez-Urrestarazu, L., & López-Rodríguez, D. (2025). URSUS_LST: URban SUStainability intelligent system for predicting the impact of urban green infrastructure on land surface temperatures. Environmental Modelling & Software, 186, 106364. 1364-8152 https://hdl.handle.net/10630/38471 10.1016/J.ENVSOFT.2025.106364 spa http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Elsevier