Mapping the time-varying spatial heterogeneity of temperature processes over the urban landscape of Augsburg, Germany

Wild, Matthias; Behm, Svenia; Beck, Christoph; Cyrys, Josef; Schneider, Alexandra; Wolf, Kathrin; Haupt, Harry

doi:10.1016/j.uclim.2022.101160

Cited by 6 publications

(3 citation statements)

References 63 publications

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“…ese nonmaterial cultural contents are the soul of the garden-like cultural landscape and are very important. Intangible cultural content means the important content that distinguishes the garden-type cultural landscape from other categories of cultural heritage [9].…”

Section: Characteristics Of the Garden Landscapementioning

confidence: 99%

“…. ., 24; first, the whole sample data set is clustered on the A1 dimensional group using the clustering method to obtain three classes of K m,n PSO (1,4,7,10,13,16,19,22), (2, 5, 8, 11, 14, 17 20, 23), and (3,6,9,12,15,18,21,24), and then apply the same method to cluster (1,4,7,10,13,16,19,22) first in A2 dimension to obtain one class (1,4,7,10,13,16,19,22) and cluster (2,5,8,11,14,17,20,23) in A2 dimension to obtain two classes (2,5,8,11,14,17) and (20,23), and one class (3,…”

Section: Empirical Analysismentioning

confidence: 99%

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Research on the Application of Improved K-Means Spatial Clustering Algorithm in Landscape Conservation of Gardens

Sun

Yang²

2022

Security and Communication Networks

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Garden art is the culture of generations since ancient times and is another spiritual carrier of people’s internal admiration for natural landscape, culture, and art as well as their love for living in nature and landscape. This article mainly studies the data clustering algorithm and adopts a research methodology that progresses from simple to complex. It starts by establishing a spatial data clustering model and then clustering the low-dimensional data, and then processing the high-dimensional spatial data in the low-dimensional data set. The original K-means clustering algorithm is then improved, and the new algorithm is created by combining PSO with the K-means algorithm in the high-dimensional spatial data set. And the improved two algorithms are applied to the study of data related to landscape conservation sites, and the powerful superiority of the improved K-means spatial clustering algorithm in this article is verified through the comparison of the algorithms.

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Section: Characteristics Of the Garden Landscapementioning

confidence: 99%

Section: Empirical Analysismentioning

confidence: 99%

Research on the Application of Improved K-Means Spatial Clustering Algorithm in Landscape Conservation of Gardens

Sun

Yang²

2022

Security and Communication Networks

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“…The N prediction errors are averaged to obtain the final prediction error. The prediction error is the difference between the model's predicted composite value and the actual composite value, which helps us to understand the model's performance on unknown data and thus assess the model's generalization ability (Wild et al, 2022). The equation used is as follows:…”

Section: ) Model Validationmentioning

confidence: 99%

Enhancing urban blue-green landscape quality assessment through hybrid Genetic Algorithm-Back Propagation (GA-BP) neural network approach: a case study in Fucheng, China

Fan,

Malik,

et al. 2024

Preprint

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This study employs an artificial neural network optimization algorithm, enhanced with a Genetic Algorithm-Back Propagation (GA-BP) network, to assess the service quality of urban water bodies and green spaces, aiming to promote healthy urban environments. From an initial set of 95 variables, 29 key variables were selected, including 17 input variables, such as water and green space area, population size, and urbanization rate, six hidden layer neurons, such as patch number, patch density, and average patch size, and one output variable for the comprehensive value of blue-green landscape quality. The results indicate that the GA-BP network achieves an average relative error of 0.94772%, which is superior to the 1.5988% of the traditional BP network. Moreover, it boasts a prediction accuracy of 90% for the comprehensive value of landscape quality from 2015 to 2022, significantly outperforming the BP network's approximate 70% accuracy. This method enhances the accuracy of landscape quality assessment but also aids in identifying crucial factors influencing quality. It provides scientific and objective guidance for future urban landscape structure and layout, contributing to high-quality urban development and the creation of exemplary living areas.

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Modeling urban air temperature using satellite-derived surface temperature, meteorological data, and local climate zone pattern—a case study in Szeged, Hungary

Guo,

Unger,

Khabibolla

et al. 2024

Theor Appl Climatol

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Urban air temperature is a crucial variable for many urban issues. However, the availability of urban air temperature is often limited due to the deficiency of meteorological stations, especially in urban areas with heterogeneous land cover. Many studies have developed different methods to estimate urban air temperature. However, meteorological variables and local climate zone (LCZ) have been less used in this topic. Our study developed a new method to estimate urban air temperature in canopy layer during clear sky days by integrating land surface temperature (LST) from MODIS, meteorological variables based on reanalysis data, and LCZ data in Szeged, Hungary. Random forest algorithms were used for developing the estimation model. We focused on four seasons and distinguished between daytime and nighttime situations. The cross-validation results showed that our method can effectively estimate urban air temperature, with average daytime and nighttime root mean square error (RMSE) of 0.5 ℃ (R2 = 0.99) and 0.9 ℃ (R2 = 0.95), respectively. The results based on a test dataset from 2018 to 2019 indicated that the optimal model selected by cross-validation had the best performance in summer, with time-synchronous RMSE of 2.1 ℃ (R2 = 0.6, daytime) and 2.2 ℃ (R2 = 0.86, nighttime) and seasonal mean RMSE of 1.5 ℃ (R2 = 0.34, daytime) and 1.2 ℃ (R2 = 0.74, nighttime). In addition, we found that LCZ was more important at night, while meteorological data contributed more to the model during the daytime, which revealed the temporal mechanisms of the effect of these two variables on air temperature estimation. Our study provides a novel and reliable method and tool to explore the urban thermal environment for urban researchers.

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Mapping the time-varying spatial heterogeneity of temperature processes over the urban landscape of Augsburg, Germany

Cited by 6 publications

References 63 publications

Research on the Application of Improved K-Means Spatial Clustering Algorithm in Landscape Conservation of Gardens

Research on the Application of Improved K-Means Spatial Clustering Algorithm in Landscape Conservation of Gardens

Enhancing urban blue-green landscape quality assessment through hybrid Genetic Algorithm-Back Propagation (GA-BP) neural network approach: a case study in Fucheng, China

Modeling urban air temperature using satellite-derived surface temperature, meteorological data, and local climate zone pattern—a case study in Szeged, Hungary

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