Predicting vulnerability through hybrid modeling combining GAM and XGBoost - A case of affected population vulnerability to tropical cyclone in Hainan Province-Reference-Cited by-同舟云学术

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Predicting vulnerability through hybrid modeling combining GAM and XGBoost - A case of affected population vulnerability to tropical cyclone in Hainan Province

Published:2024-09 Issue: Volume:111 Page:104732
ISSN:2212-4209
Container-title:International Journal of Disaster Risk Reduction
language:en
Short-container-title:International Journal of Disaster Risk Reduction

Author:

Meng Chenna^ORCID,Xu Wei,Qiao Yu^ORCID,Qin Lianjie,Su Peng,Liao Xinli

Publisher

Elsevier BV

Reference44 articles.

1. Global Tropical Cyclone Damages and Fatalities under Climate Change: an Updated Assessment. Hurricane Risk;Bakkensen,2019

2. Tropical cyclones and climate change assessment: Part II: projected response to anthropogenic warming;Knutson;Bull. Am. Meteorol. Soc.,2020

3. Double benefit of limiting global warming for tropical cyclone exposure;Geiger;Nat. Clim. Change,2021

4. A global historical data set of tropical cyclone exposure (TCE-DAT);Geiger;Earth Syst. Sci. Data,2018

5. Tropical cyclone risk assessment for China at the provincial level based on clustering analysis;Cai;Geomatics, Nat. Hazards Risk,2020

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