首页|Reports on Machine Learning Findings from Guangzhou University Provide New Insig hts (Exploring the relationship between air temperature and urban morphology fac tors using machine learning under local climate zones)
Reports on Machine Learning Findings from Guangzhou University Provide New Insig hts (Exploring the relationship between air temperature and urban morphology fac tors using machine learning under local climate zones)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news reporting out of Guangzhou, People's Re public of China, by NewsRx editors, research stated, "Urban microclimate faces s erious challenges due to increased urbanization and frequent heatwave events. Ma ny studies focused on investigating the holistic quantitative relationships betw een urban morphology factors and heat island intensity at the city scale, but le ss effort has been devoted to exploring the relationships on a block scale. Addi tionally, there is a lack of fast prediction methods for urban microclimate for local climate zones (LCZ) planning and design." Financial supporters for this research include National Natural Science Foundati on of China. The news correspondents obtained a quote from the research from Guangzhou Univer sity: "To address these challenges, this study proposes a Long Short-Term Memory Networks (LSTM) model to predict the effects of urban morphology factors on the air temperature under local climate zones. The effects of the spatial morpholog y features on the air temperature were characterized and quantified employing a postinterpretation method. The Pearl River New Town (PRNT), the downtown area o f Guangzhou, China, was considered as the research area for the model implementa tion. The results showed that air temperature prediction accuracy is the best wh en using the historical three-time step data, with R2 of 0.975. LCZ A has the hi ghest prediction accuracy, with an R2 of 0.990. LCZ 5 has the lowest accuracy, w ith an R2 of 0.881. Moreover, the effect of urban morphology factors on air temp erature was found to be greater than the effect of land cover type. In this rega rd, the sky view factor (SVF) has the highest impact, followed by the aspect rat io (AR) and the pervious surface fraction (PSF). Nevertheless, the warming effec t in built type was stronger than that in land cover. During the heatwave period, the maximum and minimum temperature changes were recorded in LCZ 4 and LCZ A, respectively, with values of 9.7 °C and 8.6 °C. It was shown that low-rise areas are more resilient than high-rise areas during heatwave periods. This is becaus e low-rise areas generally exhibit a smaller increase in air temperature."
Guangzhou UniversityGuangzhouPeople' s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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