Advanced susceptibility analysis of ground deformation disasters using large language models and machine learning: A Hangzhou City case study.

To address the prevailing scenario where comprehensive susceptibility assessments of ground deformation disasters primarily rely on knowledge-driven models, with weight judgments largely founded on expert subjective assessments, this study initially explores the feasibility of integrating data-drive...

Ausführliche Beschreibung

Bibliografische Detailangaben

Hauptverfasser:	Bofan Yu, Huaixue Xing, Weiya Ge, Liling Zhou, Jiaxing Yan, Yun-An Li
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Public Library of Science (PLoS) 2024-01-01
Schriftenreihe:	PLoS ONE
Online-Zugang:	https://doi.org/10.1371/journal.pone.0310724
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