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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Online Access: | https://doi.org/10.1088/1748-9326/abd431 https://doaj.org/article/3b02121ca3214a909c903346dd4f26a7 |
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ftdoajarticles:oai:doaj.org/article:3b02121ca3214a909c903346dd4f26a7 2023-09-05T13:22:31+02:00 Using geographically weighted regression to predict the spatial distribution of frozen ground temperature: a case in the Qinghai–Tibet Plateau Rui Zhao Mingxing Yao Linchuan Yang Hua Qi Xianglian Meng Fujun Zhou 2021-01-01T00:00:00Z https://doi.org/10.1088/1748-9326/abd431 https://doaj.org/article/3b02121ca3214a909c903346dd4f26a7 EN eng IOP Publishing https://doi.org/10.1088/1748-9326/abd431 https://doaj.org/toc/1748-9326 doi:10.1088/1748-9326/abd431 1748-9326 https://doaj.org/article/3b02121ca3214a909c903346dd4f26a7 Environmental Research Letters, Vol 16, Iss 2, p 024003 (2021) 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 article 2021 ftdoajarticles https://doi.org/10.1088/1748-9326/abd431 2023-08-13T00:37:20Z 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. Article in Journal/Newspaper permafrost Directory of Open Access Journals: DOAJ Articles Environmental Research Letters 16 2 024003 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
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 |
spellingShingle |
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 Rui Zhao Mingxing Yao Linchuan Yang Hua Qi Xianglian Meng Fujun Zhou Using geographically weighted regression to predict the spatial distribution of frozen ground temperature: a case in the Qinghai–Tibet Plateau |
topic_facet |
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 |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Rui Zhao Mingxing Yao Linchuan Yang Hua Qi Xianglian Meng Fujun Zhou |
author_facet |
Rui Zhao Mingxing Yao Linchuan Yang Hua Qi Xianglian Meng Fujun Zhou |
author_sort |
Rui Zhao |
title |
Using geographically weighted regression to predict the spatial distribution of frozen ground temperature: a case in the Qinghai–Tibet Plateau |
title_short |
Using geographically weighted regression to predict the spatial distribution of frozen ground temperature: a case in the Qinghai–Tibet Plateau |
title_full |
Using geographically weighted regression to predict the spatial distribution of frozen ground temperature: a case in the Qinghai–Tibet Plateau |
title_fullStr |
Using geographically weighted regression to predict the spatial distribution of frozen ground temperature: a case in the Qinghai–Tibet Plateau |
title_full_unstemmed |
Using geographically weighted regression to predict the spatial distribution of frozen ground temperature: a case in the Qinghai–Tibet Plateau |
title_sort |
using geographically weighted regression to predict the spatial distribution of frozen ground temperature: a case in the qinghai–tibet plateau |
publisher |
IOP Publishing |
publishDate |
2021 |
url |
https://doi.org/10.1088/1748-9326/abd431 https://doaj.org/article/3b02121ca3214a909c903346dd4f26a7 |
genre |
permafrost |
genre_facet |
permafrost |
op_source |
Environmental Research Letters, Vol 16, Iss 2, p 024003 (2021) |
op_relation |
https://doi.org/10.1088/1748-9326/abd431 https://doaj.org/toc/1748-9326 doi:10.1088/1748-9326/abd431 1748-9326 https://doaj.org/article/3b02121ca3214a909c903346dd4f26a7 |
op_doi |
https://doi.org/10.1088/1748-9326/abd431 |
container_title |
Environmental Research Letters |
container_volume |
16 |
container_issue |
2 |
container_start_page |
024003 |
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1776203043510943744 |