Using geographically weighted regression to predict the spatial distribution of frozen ground temperature: a case in the Qinghai–Tibet Plateau

This paper combines the use of principal component analysis (PCA) and the geographically weighted regression (GWR) model to predict the spatial distribution of frozen ground temperature. PCA is used to reduce the multicollinearity among covariates, while the GWR model is used to address the spatiall...

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Bibliographic Details
Published in:Environmental Research Letters
Main Authors: Rui Zhao, Mingxing Yao, Linchuan Yang, Hua Qi, Xianglian Meng, Fujun Zhou
Format: Article in Journal/Newspaper
Language:English
Published: IOP Publishing 2021
Subjects:
geographically weighted regression
principal component analysis
frozen ground
ground temperature
permafrost
spatial heterogeneity
Environmental technology. Sanitary engineering
TD1-1066
Environmental sciences
GE1-350
Science
Q
Physics
QC1-999
Online Access:https://doi.org/10.1088/1748-9326/abd431
https://doaj.org/article/3b02121ca3214a909c903346dd4f26a7
Description
Summary:This paper combines the use of principal component analysis (PCA) and the geographically weighted regression (GWR) model to predict the spatial distribution of frozen ground temperature. PCA is used to reduce the multicollinearity among covariates, while the GWR model is used to address the spatially non-stationary relationship between frozen ground temperature and its predictors, such as air temperature, latitude, longitude, and vegetation cover. Our approach is applied in a typical permafrost area in the Qinghai–Tibet Plateau, Western China. The result demonstrates the applicability of our approach in the spatial distribution of frozen ground temperature and shows that the approach can be used for analysis and prediction. This study offers insight into temperature monitoring of frozen ground, which helps prevent regional geological disasters.

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