| Autor: |
Avila-Aceves, Evangelina, Plata-Rocha, Wenseslao, Monjardin-Armenta, Sergio Alberto, Rangel-Peraza, Jesús Gabriel |
| Předmět: |
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| Zdroj: |
Stochastic Environmental Research & Risk Assessment; Nov2023, Vol. 37 Issue 11, p4109-4128, 20p |
| Abstrakt: |
Floods are one of the most frequent, dangerous natural disasters globally. During the period from 1990 to 2020, more than 50% of the world's recorded disasters were related to floods. This problem stems largely from the inadequate planning and economic circumstances of human settlements in flood-prone plains. Geospatial modelling can be a powerful tool for large-scale flood modelling. The hydrological and hydraulic models theory, GIS-based multi-criteria evaluation techniques, and machine learning algorithms for flood simulation are presented. The most used techniques and methodologies for the geospatial simulation of floods in the last decade are presented. This paper also shows the input data requirements and the algorithms used for each geospatial technique and a description of the tools and some relevant examples of geospatial flood studies are given. A comprehensive assessment of the characteristics of the flood models is presented based on its modelling approach, either for flood susceptibility, hazard, vulnerability, or risk. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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