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Flood vulnerability and buildings’ flood exposure assessment in a densely urbanised city: comparative analysis of three scenarios using a neural network approach / Quoc Bao Pham in Natural Hazards, vol 113 n° 2 (September 2022)  

Public [article]  inNatural Hazards > vol 113 n° 2 (September 2022) . - pp 1043 - 1081 Titre : Flood vulnerability and buildings’ flood exposure assessment in a densely urbanised city: comparative analysis of three scenarios using a neural network approach Type de document : Article/Communication Auteurs : Quoc Bao Pham, Auteur ; Sk Ajim Ali, Auteur ; Elzbieta Bielecka, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1043 - 1081 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] aléa [Termes IGN] apprentissage profond [Termes IGN] cartographie des risques [Termes IGN] classification par Perceptron multicouche [Termes IGN] inondation [Termes IGN] modèle de simulation [Termes IGN] prévention des risques [Termes IGN] processus de hiérarchisation analytique [Termes IGN] régression logistique [Termes IGN] réseau neuronal artificiel [Termes IGN] système d'information géographique [Termes IGN] Varsovie (Pologne) [Termes IGN] vulnérabilité [Termes IGN] zone urbaine dense Résumé : (auteur) Advances in the availability of multi-sensor, remote sensing-derived datasets, and machine learning algorithms can now provide an unprecedented possibility to predict flood events and risk. Therefore, this study was undertaken to develop a flood vulnerability map and to assess the exposure of buildings to flood risk in Warsaw, the capital of Poland. This goal was pursued in four research phases. The thirteen flood predictors were evaluated using information gain ratio (IGR), and finally reduced to eight of the most causative ones and used for flood vulnerability mapping with three machine learning algorithms, Artificial Neural Network Multi-Layer Perceptron (ANN/MLP), Deep Learning Neural Network based approach—DL4j (DLNN-DL4j) and Bayesian Logistic Regression (BLR). These algorithms show a good predictive performance with the receiver operating curve (ROC) value of 0.851, 0.877 and 0.697, respectively. The buildings’ exposure to flood was assessed in line with criteria established in European and national legal regulations. The introduced new buildings' flood hazard index (BFH) revealed a significant similarity of potential flood risk for both models, highlighting the greatest risk in zones with high vulnerability to flooding. Depending on the method used, the BFH value was 0.54 (ANN), 0.52 (DLNNs) or 0.64 (BLR). The holistic approach proposed in this study could assist local authorities in improving flood management. Numéro de notice : A2022-705 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1007/s11069-022-05336-5 Date de publication en ligne : 05/04/2022 En ligne : https://doi.org/10.1007/s11069-022-05336-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101569 [article]

Redistribution population data across a regular spatial grid according to buildings characteristics / Beata Calka in Geodesy and cartography, vol 65 n° 2 (December 2016)  

Public [article]  inGeodesy and cartography > vol 65 n° 2 (December 2016) . - pp 149 - 162 Titre : Redistribution population data across a regular spatial grid according to buildings characteristics Type de document : Article/Communication Auteurs : Beata Calka, Auteur ; Elzbieta Bielecka, Auteur ; Katarzyna Zdunkiewicz, Auteur Année de publication : 2016 Article en page(s) : pp 149 - 162 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] base de données topographiques [Termes IGN] bati [Termes IGN] données démographiques [Termes IGN] géolocalisation [Termes IGN] habitat (urbanisme) [Termes IGN] maille carrée [Termes IGN] Pologne [Termes IGN] répartition géographique Résumé : (auteur) Population data are generally provided by state census organisations at the predefined census enumeration units. However, these datasets very are often required at userdefined spatial units that differ from the census output levels. A number of population estimation techniques have been developed to address these problems. This article is one of those attempts aimed at improving county level population estimates by using spatial disaggregation models with support of buildings characteristic, derived from national topographic database, and average area of a flat. The experimental gridded population surface was created for Opatów county, sparsely populated rural region located in Central Poland. The method relies on geolocation of population counts in buildings, taking into account the building volume and structural building type and then aggregation the people total in 1 km quadrilateral grid. The overall quality of population distribution surface expressed by the mean of RMSE equals 9 persons, and the MAE equals 0.01. We also discovered that nearly 20% of total county area is unpopulated and 80% of people lived on 33% of the county territory. Numéro de notice : A2016-978 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1515/geocart-2016-0011 En ligne : https://doi.org/10.1515/geocart-2016-0011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83695 [article]

Geographical data sets fitness of use evaluation / Elzbieta Bielecka in Geodetski vestnik, vol 59 n° 2 (June - August 2015)  

Public [article]  inGeodetski vestnik > vol 59 n° 2 (June - August 2015) . - pp 335 - 348 Titre : Geographical data sets fitness of use evaluation Titre original : Ocena primernosti uporabe prostorskih podatkovnih Type de document : Article/Communication Auteurs : Elzbieta Bielecka, Auteur Année de publication : 2015 Article en page(s) : pp 335 - 348 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] base de données topographiques [Termes IGN] évaluation [Termes IGN] métadonnées [Termes IGN] métadonnées géographiques [Termes IGN] Pologne [Termes IGN] qualité des données [Termes IGN] utilisateur Résumé : (auteur) The large number of commonly available geographical data sets means that users of this data face a difficult choice in selecting the set that best meets their requirements. In theory, metadata is helpful in this, but many studies suggest that the metadata created by data producers is incomprehensible to average users. The article aims to identify the essential information that users need to acquire the geographical data set that fits their needs. This information is then compared with the information that users can obtain from metadata, product data specifications, and the data itself. As a result of a survey the most important data quality elements were identified, as well as some information pertinent to users that is missing in metadata. The users stressed that a lack of value for optional attributes considerably decreases the informative value and fitness for use of existing data sets. This was also observed while analyzing the building thematic data layer, which is a part of The Polish National Topographic Database. The research shows that data quality is diversified within a database, and it may happen that for some subsets of data, quality criteria are not met. Finally, two data quality subelements – optional attribute and void value - were proposed, which will overcome some difficulties in assessing the fitness for use of data. Numéro de notice : A2015-257 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.15292/geodetski-vestnik.2015.02.335-348 En ligne : http://dx.doi.org/10.15292/geodetski-vestnik.2015.02.335-348 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76283 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
139-2015021	RAB	Revue	Centre de documentation	En réserve L003	Disponible