| Titre | Multi-scale flood mapping under climate change scenarios in hexagonal discrete global grids |
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| Auteur | Li, M; McGrath, H ; Stefanakis, E |
| Source | ISPRS International Journal of Geo-Information vol. 11, issue 12, 2022 p. 1-26, https://doi.org/10.3390/ijgi11120627  Accès ouvert |
| Image |  |
| Année | 2022 |
| Séries alt. | Ressources naturelles Canada, Contribution externe 20220363 |
| Éditeur | MDPI |
| Document | publication en série |
| Lang. | anglais |
| DOI | https://doi.org/10.3390/ijgi11120627 |
| Media | papier; numérique; en ligne |
| Formats | pdf |
| Province | Nouveau-Brunswick |
| Lat/Long OENS | -67.0000 -66.0000 46.0000 45.0000 |
| Sujets | inondations; plaines d'inondation; établissement de modèles; échantillonage par grille; climat; effets climatiques; Changement climatique; l'apprentissage machine; Système d'information géographique;
Géologie; géologie de l'environnement |
| Illustrations | tableaux; imagerie satellitaire; graphiques; organigrammes; histogrammes; cartes de localisation |
| Programme | Géobase 2.0 Exploitation des
données hautes résolutions |
| Diffusé | 2022 12 17 |
| Résumé | (disponible en anglais seulement)
Among the most prevalent natural hazards, flooding has been threatening human lives and properties. Robust flood simulation is required for effective response
and prevention. Machine learning is widely used in flood modeling due to its high performance and scalability. Nonetheless, data pre-processing of heterogeneous sources can be cumbersome, and traditional data processing and modeling have been limited
to a single resolution. This study employed an Icosahedral Snyder Equal Area Aperture 3 Hexagonal Discrete Global Grid System (ISEA3H DGGS) as a scalable, standard spatial framework for computation, integration, and analysis of multi-source
geospatial data. We managed to incorporate external machine learning algorithms with a DGGS-based data framework, and project future flood risks under multiple climate change scenarios for southern New Brunswick, Canada. A total of 32 explanatory
factors including topographical, hydrological, geomorphic, meteorological, and anthropogenic were investigated. Results showed that low elevation and proximity to permanent waterbodies were primary factors of flooding events, and rising spring
temperatures can increase flood risk. Flooding extent was predicted to occupy 135-203% of the 2019 flood area, one of the most recent major flooding events, by the year 2100. Our results assisted in understanding the potential impact of climate
change on flood risk, and indicated the feasibility of DGGS as the standard data fabric for heterogeneous data integration and incorporated in multi-scale data mining. |
| Sommaire | (Résumé en langage clair et simple, non publié)
Cartographie de la susceptibilité aux inondations dans le cadre de scénarios climatiques futurs dans un DGGS, exploration de données
multi-échelles. |
| GEOSCAN ID | 330904 |
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