Mechanisms of non-stationary influence of urban form on the diurnal thermal environment based on machine learning and MGWR analysis

Zhao, Jun; Guo, Fei; Zhang, Hongchi; Dong, Jing

doi:10.1016/j.scs.2024.105194

Cited by 25 publications

(3 citation statements)

References 78 publications

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“…It has good explanatory power in geographical spatial research. The MGWR model evolved from the GWR model, improving the fixed bandwidth aspect of the GWR model, thereby making the model calculations more accurate [31,58]. The specific calculation process is detailed in the Supplementary Materials.…”

Section: Traditional Geographic Spatial Regression Models: Gwr and Mgwrmentioning

confidence: 99%

“…Among these methods, multiple linear regression and geographic detector models mainly focus on investigating the influence of driving factors on the LST [27]. GWR concentrates on the spatial dimension, providing strong explanatory power at the geographic level [31]. In contrast, machine learning excels in explaining large data models [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

“…: Table describing the indicators based on Fragstats software. References[27,31,38,[51][52][53][54][55][56][57][58] are cited in the Supplementary Materials. C.X.…”

mentioning

confidence: 99%

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Comparative Analysis of the Seasonal Driving Factors of the Urban Heat Environment Using Machine Learning: Evidence from the Wuhan Urban Agglomeration, China, 2020

Xu,

Huang,

Zhang

2024

Atmosphere

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With the ongoing advancement of globalization significantly impacting the ecological environment, the continuous rise in the Land Surface Temperature (LST) is increasingly jeopardizing human production and living conditions. This study aims to investigate the seasonal variations in the LST and its driving factors using mathematical models. Taking the Wuhan Urban Agglomeration (WHUA) as a case study, it explores the seasonal characteristics of the LST and employs Principal Component Analysis (PCA) to categorize the driving factors. Additionally, it compares traditional models with machine-learning models to select the optimal model for this investigation. The main conclusions are as follows. (1) The WHUA’s LST exhibits significant differences among seasons and demonstrates distinct spatial-clustering characteristics in different seasons. (2) Compared to traditional geographic spatial models, Extreme Gradient Boosting (XGBoost) shows better explanatory power in investigating the driving effects of the LST. (3) Human Activity (HA) dominates the influence throughout the year and shows a significant positive correlation with the LST; Physical Geography (PG) exhibits a negative correlation with the LST; Climate and Weather (CW) show a similar variation to the PG, peaking in the transition; and the Landscape Pattern (LP) shows a weak positive correlation with the LST, peaking in winter while being relatively inconspicuous in summer and the transition. Finally, through comparative analysis of multiple driving factors and models, this study constructs a framework for exploring the seasonal features and driving factors of the LST, aiming to provide references and guidance for the development of the WHUA and similar regions.

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Section: Traditional Geographic Spatial Regression Models: Gwr and Mgwrmentioning

confidence: 99%