Descripteur
Documents disponibles dans cette catégorie (1163)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB / Mahya Norallahi in Natural Hazards, vol 106 n° 1 (March 2021)
[article]
Titre : Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB Type de document : Article/Communication Auteurs : Mahya Norallahi, Auteur ; Hesam Seyed Kaboli, Auteur Année de publication : 2021 Article en page(s) : pp119 - 137 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] algorithme génétique
[Termes IGN] apprentissage automatique
[Termes IGN] cartographie des risques
[Termes IGN] classification bayesienne
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] entropie maximale
[Termes IGN] inondation
[Termes IGN] Iran
[Termes IGN] zone urbaineRésumé : (auteur) Rapid urban development, increasing impermeable surfaces, poor drainage system and changes in extreme precipitations are the most important factors that nowadays lead to increased urban flooding and it has become an urban problem. Urban flood mapping and its use in making an urban development plan can reduce flood damages and losses. Constantly producing urban flood hazard maps using models that rely on the availability of detailed hydraulic-hydrological data is a major challenge especially in developing countries. In this study, urban flood hazard map was produced with limited data using three machine learning models: Genetic Algorithm Rule-Set Production, Maximum Entropy (MaxEnt), Random Forest (RF) and Naïve Bayes for Kermanshah city, Iran. The flood hazard predicting factors used in modeling were: slope, land use, precipitation, distance to river, distance to channel, curve number (CN) and elevation. Flood inventory map was produced based on available reports and field surveys, that 117 flooded points and 163 non-flooded points were identified. Models performance was evaluated based on area under the receiver-operator characteristic curve (AUC-ROC), Kappa statistic and hits and miss analysis. The results show that RF model (AUC-ROC = 99.5%, Kappa = 98%, Accuracy = 90%, Success ratio = 99%, Threat score = 90% and Heidke skill score = 98%) performed better than other models. The results also showed that distance to canal, land use and CN have shown more contribution among others for modeling the flood and precipitation had the least effect among other factors. The findings show that machine learning methods can be a good alternative to distributed models to predict urban flood-prone areas where there are lack of detailed hydraulic and hydrological data. Numéro de notice : A2021-418 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s11069-020-04453-3 Date de publication en ligne : 04/01/2021 En ligne : https://doi.org/10.1007/s11069-020-04453-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97768
in Natural Hazards > vol 106 n° 1 (March 2021) . - pp119 - 137[article]Integrating runoff map of a spatially distributed model and thematic layers for identifying potential rainwater harvesting suitability sites using GIS techniques / Hamid Karimi in Geocarto international, vol 36 n° 3 ([15/02/2021])
[article]
Titre : Integrating runoff map of a spatially distributed model and thematic layers for identifying potential rainwater harvesting suitability sites using GIS techniques Type de document : Article/Communication Auteurs : Hamid Karimi, Auteur ; Hossein Zeinivand, Auteur Année de publication : 2021 Article en page(s) : pp 320 - 339 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] carte hydrographique
[Termes IGN] combinaison linéaire ponderée
[Termes IGN] couche thématique
[Termes IGN] eau pluviale
[Termes IGN] écoulement des eaux
[Termes IGN] étang
[Termes IGN] Iran
[Termes IGN] modèle hydrographique
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] ruissellementRésumé : (auteur) Rainwater harvesting (RWH) is one of the major techniques that is investigated in the present study using Analytic Hierarchy Process (AHP) and Weighted Linear Combination (WLC) methods as two tools for decision-making, weighting and combining different thematic layers include land use, slope, drainage density and hydrological soil groups (HSG). The runoff map obtained by the distributed spatial-physical WetSpa model is considered as a useful layer that is integrated with other thematic layers in the geographic information system (GIS) environment for identifying RWH sites. Kakareza watershed (1132 km2) in Iran was selected as a study area to carry out the foregoing approach. The results showed that 256 km2 of the study area is good for RWH, 360 km2 is moderate and 516 km2 is poor. Thus, about 22.61% (256 km2) of Kakareza watershed is highly suitable for farm ponds. This article recommends the RWH suitable sites to a judicious decision for better water management in the area. Numéro de notice : A2021-141 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1608590 Date de publication en ligne : 28/05/2019 En ligne : https://doi.org/10.1080/10106049.2019.1608590 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97037
in Geocarto international > vol 36 n° 3 [15/02/2021] . - pp 320 - 339[article]An improved rainfall-threshold approach for robust prediction and warning of flood and flash flood hazards / Geraldo Moura Ramos Filho in Natural Hazards, Vol 105 n° 3 (February 2021)
[article]
Titre : An improved rainfall-threshold approach for robust prediction and warning of flood and flash flood hazards Type de document : Article/Communication Auteurs : Geraldo Moura Ramos Filho, Auteur ; Victor Hugo Rabelo Coelho, Auteur ; Emerson da Silva Freitas, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2409 - 2429 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] crue
[Termes IGN] Indice de précipitations antérieures
[Termes IGN] indice de risque
[Termes IGN] inondation
[Termes IGN] méthode robuste
[Termes IGN] prévention des risques
[Termes IGN] risque naturel
[Termes IGN] Sao Paulo
[Termes IGN] seuillage
[Termes IGN] surveillance hydrologiqueRésumé : (auteur) This paper presents an improved method of using threshold of peak rainfall intensity for robust flood/flash flood evaluation and warnings in the state of São Paulo, Brazil. The improvements involve the use of two tolerance levels and the delineating of an intermediate threshold by incorporating an exponential curve that relates rainfall intensity and Antecedent Precipitation Index (API). The application of the tolerance levels presents an average increase of 14% in the Probability of Detection (POD) of flood and flash flood occurrences above the upper threshold. Moreover, a considerable exclusion (63%) of non-occurrences of floods and flash floods in between the two thresholds significantly reduce the number of false alarms. The intermediate threshold using the exponential curves also exhibits improvements for almost all time steps of both hydrological hazards, with the best results found for floods correlating 8-h peak intensity and 8 days API, with POD and Positive Predictive Value (PPV) values equal to 81% and 82%, respectively. This study provides strong indications that the new proposed rainfall threshold-based approach can help reduce the uncertainties in predicting the occurrences of floods and flash floods. Numéro de notice : A2020-204 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s11069-020-04405-x Date de publication en ligne : 03/11/2020 En ligne : https://doi.org/10.1007/s11069-020-04405-x Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97167
in Natural Hazards > Vol 105 n° 3 (February 2021) . - pp 2409 - 2429[article]A dynamic bidirectional coupled surface flow model for flood inundation simulation / Chunbo Jiang in Natural Hazards and Earth System Sciences, Vol 21 n° 2 (February 2021)
[article]
Titre : A dynamic bidirectional coupled surface flow model for flood inundation simulation Type de document : Article/Communication Auteurs : Chunbo Jiang, Auteur ; Qi Zhou, Auteur ; Wangyang Yu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 497 - 515 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] Chine
[Termes IGN] crue
[Termes IGN] inondation
[Termes IGN] modèle de simulation
[Termes IGN] modèle dynamique
[Termes IGN] modèle hydrographique
[Termes IGN] prévention des risquesRésumé : (auteur) Flood disasters frequently threaten people and property all over the world. Therefore, an effective numerical model is required to predict the impacts of floods. In this study, a dynamic bidirectional coupled hydrologic–hydrodynamic model (DBCM) is developed with the implementation of characteristic wave theory, in which the boundary between these two models can dynamically adapt according to local flow conditions. The proposed model accounts for both mass and momentum transfer on the coupling boundary and was validated via several benchmark tests. The results show that the DBCM can effectively reproduce the process of flood propagation and also account for surface flow interaction between non-inundation and inundation regions. The DBCM was implemented for the floods simulation that occurred at Helin Town located in Chongqing, China, which shows the capability of the model for flood risk early warning and future management. Numéro de notice : A2021-168 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.5194/nhess-21-497-2021 Date de publication en ligne : 03/02/2021 En ligne : https://doi.org/10.5194/nhess-21-497-2021 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97107
in Natural Hazards and Earth System Sciences > Vol 21 n° 2 (February 2021) . - pp 497 - 515[article]Optimizing flood mapping using multi-synthetic aperture radar images for regions of the lower mekong basin in Vietnam / Vu Anh Tuan in European journal of remote sensing, vol 54 n° 1 (2021)
[article]
Titre : Optimizing flood mapping using multi-synthetic aperture radar images for regions of the lower mekong basin in Vietnam Type de document : Article/Communication Auteurs : Vu Anh Tuan, Auteur ; Nguyen Hong Quang, Auteur ; le Thi Thu Hang, Auteur Année de publication : 2021 Article en page(s) : pp 13 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bande L
[Termes IGN] cartographie des risques
[Termes IGN] crue
[Termes IGN] image ALOS
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] inondation
[Termes IGN] Mekong (fleuve)
[Termes IGN] optimisation spatiale
[Termes IGN] surveillance hydrologique
[Termes IGN] Viet NamRésumé : (auteur) One major characteristic of floods is flood extent. Information on this characteristic is indispensable for flood monitoring. Recently, synthetic aperture radar (SAR) data have been increasing in quality and quantity. This allows more flood studies conducted over large areas regardless of cloud and weather conditions and provides advantages including clear surface water classification based on SAR scattering mechanisms for low values (open water) and high values (inundated vegetation, etc.). However, challenges remain due to sources of uncertainties, such as atmospheric disturbances and vegetation masking parts of water surfaces. Therefore, in this study, we aim to optimize flood mapping processes on flooded vegetation that generated high-value pixels based on a SAR scattering mechanism called double bounce that classifies vegetative flooded water in L-band SAR images. This optimization is nearly impossible using Sentinel-1 scenes. Backscattering of time-series Sentinel-1 and ALOS-2 images acquired for the 2018 and 2019 flood season was analysed, thresholded and hybridized for flood mapping of a study site in the Tam Nong district of the Dong Thap Province of Vietnam. We found that the accuracy of SAR flood maps was improved compared to ground truth data when the SAR-extracted vegetative-flooded plains were considered flooded. Numéro de notice : A2021-139 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/22797254.2020.1859340 Date de publication en ligne : 30/12/2020 En ligne : https://doi.org/10.1080/22797254.2020.1859340 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97015
in European journal of remote sensing > vol 54 n° 1 (2021) . - pp 13 - 28[article]PermalinkApport des données satellitaires Sentinel-1 et Sentinel-2 pour la détection des surfaces irriguées et l'estimation des besoins et des consommations en eau des cultures d'été dans les zones tempérées / Yann Pageot (2021)PermalinkApport de la photogrammétrie satellite pour la modélisation du manteau neigeux / César Deschamps-Berger (2021)PermalinkCalcul de la largeur à pleins bords de grands cours d’eau à partir de MNT LiDAR / Nicolas Fermen (2021)PermalinkPermalinkDynamics of inundation events in the rivers-estuaries-ocean continuum in Bengal delta : synergy between hydrodynamic modelling and spaceborne remote sensing / Md Jamal Uddin Kahn (2021)PermalinkFlood mapping from radar remote sensing using automated image classification techniques / Lisa Landuyt (2021)PermalinkGeospatial analysis of September, 2019 floods in the lower gangetic plains of Bihar using multi-temporal satellites and river gauge data / C.M. Bhatt in Geomatics, Natural Hazards and Risk, vol 12 n° 1 (2021)PermalinkPermalinkLes impacts spatiaux du changement climatique / Denis Mercier (2021)Permalink