A comprehensive framework for assessing the spatial drivers of flood disasters using an optimal Parameter-based geographical Detector–machine learning coupled model-Reference-Cited by-同舟云学术

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A comprehensive framework for assessing the spatial drivers of flood disasters using an optimal Parameter-based geographical Detector–machine learning coupled model

Published:2024-11 Issue:6 Volume:15 Page:101889
ISSN:1674-9871
Container-title:Geoscience Frontiers
language:en
Short-container-title:Geoscience Frontiers

Author:

Yang Luyi,Ji Xuan^ORCID,Li Meng,Yang Pengwu,Jiang Wei,Chen Linyan^ORCID,Yang Chuanjian^ORCID,Sun Cezong,Li Yungang

Publisher

Elsevier BV

Reference76 articles.

1. Regionalization of watersheds by finite mixture models;Ahani;J. Hydrol.,2020

2. Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with SHapley Additive exPlanations;Aydin;Nat. Hazards,2023

3. Regionalization of geographical space according to selected topographic factors in reference to spatial distribution of precipitation: application of artificial neural networks in GIS;Bac-Bronowicz;Environ. Earth Sci.,2018

4. Does forest cover help prevent flood damage? Empirical evidence from India;Bhattacharjee;Glob. Environ. Chang.,2018

5. Flood risk assessment based on hydrodynamic model and fuzzy comprehensive evaluation with GIS technique;Cai;Int. J. Disaster Risk Reduct.,2019

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