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Enregistrement W2948007733 · doi:10.3390/ijgi8060250

A GIS Tool for Mapping Dam-Break Flood Hazards in Italy

2019· article· en· W2948007733 sur OpenAlex

Pourquoi ce travail est dans la base

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affAu moins un auteur déclare une institution canadienne dans l'instantané OpenAlex épinglé.

Notice bibliographique

RevueISPRS International Journal of Geo-Information · 2019
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueFlood Risk Assessment and Management
Établissements canadiensMcGill University
Organismes subventionnairesMinistero dello Sviluppo Economico
Mots-clésFlood mythHydrographDam failureDigital elevation modelGeospatial analysisComputer scienceGeographic information systemEnvironmental scienceDam breakGeomaticsFlood mitigationCivil engineeringHydrology (agriculture)GeologyGeotechnical engineeringEngineeringGeographyRemote sensing

Résumé

récupéré en direct d'OpenAlex

Mapping the delineation of areas that are flooded due to water control infrastructure failure is a critical issue. Practical difficulties often present challenges to the accurate and effective analysis of dam-break hazard areas. Such studies are expensive, lengthy, and require large volumes of incoming data and refined technical skills. The creation of cost-efficient geospatial tools provides rapid and inexpensive estimates of instantaneous dam-break (due to structural failure) flooded areas that complement, but do not replace, the results of hydrodynamic simulations. The current study implements a Geographic Information System (GIS) based method that can provide useful information regarding the delineation of dam-break flood-prone areas in both data-scarce environments and transboundary regions, in the absence of detailed studies. Moreover, the proposed tool enables, without advanced technical skills, the analysis of a wide number of case studies that support the prioritization of interventions, or, in emergency situations, the simulation of numerous initial hypotheses (e.g., the modification of initial water level/volume in the case of limited dam functionality), without incurring high computational time. The proposed model is based on the commonly available data for masonry dams, i.e., dam geometry (e.g., reservoir capacity, dam height, and crest length), and a Digital Elevation Model. The model allows for rapid and cost-effective dam-break hazard mapping by evaluating three components: (i) the dam-failure discharge hydrograph, (ii) the propagation of the flood, and (iii) the delineation of flood-prone areas. The tool exhibited high accuracy and reliability in the identification of hypothetical dam-break flood-prone areas when compared to the results of traditional hydrodynamic approaches, as applied to a dam in Basilicata (Southern Italy). In particular, the over- and under-estimation rates of the proposed tool, for the San Giuliano dam, Basilicata, were evaluated by comparing its outputs with flood inundation maps that were obtained by two traditional methods whil using a one-dimensional and a two-dimensional propagation model, resulting in a specificity value of roughly 90%. These results confirm that most parts of the flood map were correctly classified as flooded by the proposed GIS model. A sensitivity value of over 75% confirms that several zones were also correctly identified as non-flooded. Moreover, the overall effectiveness and reliability of the proposed model were evaluated, for the Gleno Dam (located in the Central Italian Alps), by comparing the results of literature studies concerning the application of monodimensional numerical models and the extent of the flooded area reconstructed by the available historical information, obtaining an accuracy of around 94%. Finally, the computational efficiency of the proposed tool was tested on a demonstrative application of 250 Italian arch and gravity dams. The results, when carried out using a PC, Pentium Intel Core i5 Processor CPU 3.2 GHz, 8 GB RAM, required about 73 min, showing the potential of such a tool applied to dam-break flood mapping for a large number of dams.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,530
Score d'incertitude au seuil0,708

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,004
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,004
Tête enseignante GPT0,235
Écart entre enseignants0,231 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle