Using geographically weighted regression to predict the spatial distribution of frozen ground temperature: a case in the Qinghai–Tibet Plateau
Abstract 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...
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Online Access: | http://dx.doi.org/10.1088/1748-9326/abd431 https://iopscience.iop.org/article/10.1088/1748-9326/abd431 https://iopscience.iop.org/article/10.1088/1748-9326/abd431/pdf |
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crioppubl:10.1088/1748-9326/abd431 2024-06-02T08:13:05+00:00 Using geographically weighted regression to predict the spatial distribution of frozen ground temperature: a case in the Qinghai–Tibet Plateau Zhao, Rui Yao, Mingxing Yang, Linchuan Qi, Hua Meng, Xianglian Zhou, Fujun National Key Research and Development Program 2021 http://dx.doi.org/10.1088/1748-9326/abd431 https://iopscience.iop.org/article/10.1088/1748-9326/abd431 https://iopscience.iop.org/article/10.1088/1748-9326/abd431/pdf unknown IOP Publishing http://creativecommons.org/licenses/by/4.0 https://iopscience.iop.org/info/page/text-and-data-mining Environmental Research Letters volume 16, issue 2, page 024003 ISSN 1748-9326 journal-article 2021 crioppubl https://doi.org/10.1088/1748-9326/abd431 2024-05-07T14:03:28Z Abstract 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 IOP Publishing Environmental Research Letters 16 2 024003 |
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Abstract 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. |
author2 |
National Key Research and Development Program |
format |
Article in Journal/Newspaper |
author |
Zhao, Rui Yao, Mingxing Yang, Linchuan Qi, Hua Meng, Xianglian Zhou, Fujun |
spellingShingle |
Zhao, Rui Yao, Mingxing Yang, Linchuan Qi, Hua Meng, Xianglian Zhou, Fujun Using geographically weighted regression to predict the spatial distribution of frozen ground temperature: a case in the Qinghai–Tibet Plateau |
author_facet |
Zhao, Rui Yao, Mingxing Yang, Linchuan Qi, Hua Meng, Xianglian Zhou, Fujun |
author_sort |
Zhao, Rui |
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 |
http://dx.doi.org/10.1088/1748-9326/abd431 https://iopscience.iop.org/article/10.1088/1748-9326/abd431 https://iopscience.iop.org/article/10.1088/1748-9326/abd431/pdf |
genre |
permafrost |
genre_facet |
permafrost |
op_source |
Environmental Research Letters volume 16, issue 2, page 024003 ISSN 1748-9326 |
op_rights |
http://creativecommons.org/licenses/by/4.0 https://iopscience.iop.org/info/page/text-and-data-mining |
op_doi |
https://doi.org/10.1088/1748-9326/abd431 |
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Environmental Research Letters |
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16 |
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2 |
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024003 |
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1800759724964052992 |