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Modelling evacuation preparation time prior to floods: A machine learning approach / R. Sreejith in Sustainable Cities and Society, vol 87 (December 2022)  

Public [article]  inSustainable Cities and Society > vol 87 (December 2022) . - n° 104257 Titre : Modelling evacuation preparation time prior to floods: A machine learning approach Type de document : Article/Communication Auteurs : R. Sreejith, Auteur ; K.R. Sinimole, Auteur Année de publication : 2022 Article en page(s) : n° 104257 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] apprentissage automatique [Termes IGN] chronométrie [Termes IGN] données spatiotemporelles [Termes IGN] gestion de crise [Termes IGN] inondation [Termes IGN] Kerala (Inde ; état) [Termes IGN] modèle de simulation [Termes IGN] plan de prévention des risques [Termes IGN] questionnaire [Termes IGN] risque naturel [Termes IGN] secours d'urgence Résumé : (auteur) Flooding is a significant hazard responsible for substantial damage and risks to human life worldwide. Effective emergency evacuation to a safer location remains a concern even though the crisis can be predicted and warnings were given. During a calamity, most residents cannot quickly and securely flee. As it is crucial to start evacuation at the right time to have a safe evacuation, this study focuses on a machine learning-based model for predicting a household's evacuation preparation time in the incident of a flood. The study is based on the data collected from flood-affected people from Kerala, India, through a questionnaire. The study indicates that people's demographic, geographical and behavioural aspects, awareness of natural hazards and management are the critical components for improved emergency actions. Further, the article also analysed the characteristics of the respondents and successfully created clusters in which the respondents broadly belong, which will help the rescue team operationalize the evacuation process. Numéro de notice : A2022-819 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.scs.2022.104257 Date de publication en ligne : 14/10/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104257 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101986 [article]

Flash-flood hazard susceptibility mapping in Kangsabati River Basin, India / Rabin Chakrabortty in Geocarto international, vol 37 n° 23 ([15/10/2022])  

Public [article]  inGeocarto international > vol 37 n° 23 [15/10/2022] . - pp 6713 - 6735 Titre : Flash-flood hazard susceptibility mapping in Kangsabati River Basin, India Type de document : Article/Communication Auteurs : Rabin Chakrabortty, Auteur ; Subodh Chandra Pal, Auteur ; Fatemeh Rezaie, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 6713 - 6735 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] cartographie des risques [Termes IGN] Inde [Termes IGN] inondation [Termes IGN] mousson [Termes IGN] optimisation par essaim de particules [Termes IGN] réseau neuronal artificiel [Termes IGN] réseau neuronal profond [Termes IGN] risque naturel [Termes IGN] vulnérabilité Résumé : (auteur) Flood-susceptibility mapping is an important component of flood risk management to control the effects of natural hazards and prevention of injury. We used a remote-sensing and geographic information system (GIS) platform and a machine-learning model to develop a flood susceptibility map of Kangsabati River Basin, India where flash flood is common due to monsoon precipitation with short duration and high intensity. And in this subtropical region, climate change’s impact helps to influence the distribution of rainfall and temperature variation. We tested three models-particle swarm optimization (PSO), an artificial neural network (ANN), and a deep-leaning neural network (DLNN)-and prepared a final flood susceptibility map to classify flood-prone regions in the study area. Environmental, topographical, hydrological, and geological conditions were included in the models, and the final model was selected based on the relations between potentiality of causative factors and flood risk based on multi-collinearity analysis. The model results were validated and evaluated using the area under receiver operating characteristic (ROC) curve (AUC), which is an indicator of the current state of the environment and a value >0.95 implies a greater risk of flash floods. The AUC values for ANN, DLNN, and PSO for training datasets were 0.914, 0.920, and 0.942, respectively. Among these three models, PSO showed the best performance with an AUC value of 0.942. The PSO approach is applicable for flood susceptibility mapping of the eastern part of India, a subtropical region, to allow flood mitigation and help to improve risk management in this region. Numéro de notice : A2022-750 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1953618 Date de publication en ligne : 26/07/2021 En ligne : https://doi.org/10.1080/10106049.2021.1953618 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101742 [article]

Discontinuity interpretation and identification of potential rockfalls for high-steep slopes based on UAV nap-of-the-object photogrammetry / Wei Wang in Computers & geosciences, vol 166 (September 2022)  

Public [article]  inComputers & geosciences > vol 166 (September 2022) . - n° 105191 Titre : Discontinuity interpretation and identification of potential rockfalls for high-steep slopes based on UAV nap-of-the-object photogrammetry Type de document : Article/Communication Auteurs : Wei Wang ; Wenbo Zhao, Auteur ; Bo Chai, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 105191 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] Chine [Termes IGN] discontinuité [Termes IGN] éboulement [Termes IGN] extraction de données [Termes IGN] front rocheux [Termes IGN] image à haute résolution [Termes IGN] image captée par drone [Termes IGN] matrice [Termes IGN] pente [Termes IGN] photogrammétrie aérienne [Termes IGN] profondeur [Termes IGN] risque naturel [Termes IGN] semis de points [Termes IGN] texture d'image Résumé : (auteur) Discontinuity extraction and interpretation of fractured masses is of high importance when analyzing rock slope stability. Regarding high-steep slopes, which are areas that are difficult to reach, traditional methods to obtain discontinuities, such as the sample window method (SWM), are unlikely to be implemented, resulting in challenges for the identification of potential rockfalls. With the development of the unmanned ariel vehicle (UAV) technology, discontinuity extraction can overcome by noncontact photogrammetry. However, there is still a lack of comprehensive and practical solutions to fulfill rockfall identification from field investigation to in-door analysis. For this purpose, a practical case study was carried out in Wanzhou, Chongqing, China, where a 400 m vertical rock slope prone to rockfall was collected as a typical example. The centimeter-level 3D Textured Digital Outcrop Model (TDOM) and dense Point Cloud (PC) were established using high-resolution photos acquired by nap-of-the-object photogrammetry. The discontinuity of the fractured mass was interpreted by fully taking advantage of both 2D images (texture information-dominated) and 3D PCs (depth information-dominated). Furthermore, a new parameter rock cavity rate (RCR) and the corresponding semiautomatic extraction method based on point clouds are proposed. Subsequently, the possibility of various failure modes and their joint combinations were determined by kinematic analysis. Finally, the rock slope stability was determined using a matrix that considers the slope mass rating (SMR) value and the parameter RCR. The proposed process flow and relevant techniques in this study provide an operable and practical solution for further application regarding discontinuity interpretation and potential rockfall identification on high-steep slopes. Numéro de notice : A2022-655 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2022.105191 Date de publication en ligne : 08/07/2022 En ligne : https://doi.org/10.1016/j.cageo.2022.105191 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101504 [article]

UAV-borne, LiDAR-based elevation modelling: a method for improving local-scale urban flood risk assessment / Katerina Trepekli in Natural Hazards, vol 113 n° 1 (August 2022)  

Public [article]  inNatural Hazards > vol 113 n° 1 (August 2022) . - pp 423 - 451 Titre : UAV-borne, LiDAR-based elevation modelling: a method for improving local-scale urban flood risk assessment Type de document : Article/Communication Auteurs : Katerina Trepekli, Auteur ; Thomas Balstrøm, Auteur ; Thomas Friborg, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 423 - 451 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] antenne GNSS [Termes IGN] centrale inertielle [Termes IGN] faisceau laser [Termes IGN] Ghana [Termes IGN] inondation [Termes IGN] modèle de simulation [Termes IGN] modèle numérique de surface [Termes IGN] modèle numérique de terrain [Termes IGN] risque naturel [Termes IGN] semis de points [Termes IGN] Triangulated Irregular Network [Termes IGN] zone urbaine Résumé : (auteur) In this study, we present the first findings of the potential utility of miniaturized light and detection ranging (LiDAR) scanners mounted on unmanned aerial vehicles (UAVs) for improving urban flood modelling and assessments at the local scale. This is done by generating ultra-high spatial resolution digital terrain models (DTMs) featuring buildings and urban microtopographic structures that may affect floodwater pathways (DTMbs). The accuracy and level of detail of the flooded areas, simulated by a hydrologic screening model (Arc-Malstrøm), were vastly improved when DTMbs of 0.3 m resolution representing three urban sites surveyed by a UAV-LiDAR in Accra, Ghana, were used to supplement a 10 m resolution DTM covering the region’s entire catchment area. The generation of DTMbs necessitated the effective classification of UAV-LiDAR point clouds using a morphological and a triangulated irregular network method for hilly and flat landscapes, respectively. The UAV-LiDAR data enabled the identification of archways, boundary walls and bridges that were critical when predicting precise run-off courses that could not be projected using the coarser DTM only. Variations in a stream’s geometry due to a one-year time gap between the satellite-based and UAV-LiDAR data sets were also observed. The application of the coarser DTM produced an overestimate of water flows equal to 15% for sloping terrain and up to 62.5% for flat areas when compared to the respective run-offs simulated from the DTMbs. The application of UAV-LiDAR may enhance the effectiveness of urban planning by projecting precisely the locations, extents and run-offs of flooded areas in dynamic urban settings. Numéro de notice : A2022-704 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1007/s11069-022-05308-9 Date de publication en ligne : 22/03/2022 En ligne : https://doi.org/10.1007/s11069-022-05308-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101567 [article]

How large-scale bark beetle infestations influence the protective effects of forest stands against avalanches: A case study in the Swiss Alps / Marion E. Caduff in Forest ecology and management, vol 514 (June-15 2022)  

Public [article]  inForest ecology and management > vol 514 (June-15 2022) . - n° 120201 Titre : How large-scale bark beetle infestations influence the protective effects of forest stands against avalanches: A case study in the Swiss Alps Type de document : Article/Communication Auteurs : Marion E. Caduff, Auteur ; Natalie Brožová, Auteur ; Andrea D. Kupferschmid, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 120201 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Alpes [Termes IGN] avalanche [Termes IGN] bois mort [Termes IGN] dépérissement [Termes IGN] image à haute résolution [Termes IGN] modèle de simulation [Termes IGN] orthophotographie [Termes IGN] protection des forêts [Termes IGN] régénération (sylviculture) [Termes IGN] risque naturel [Termes IGN] santé des forêts [Termes IGN] Scolytinae [Termes IGN] Suisse [Termes IGN] xylophage Résumé : (auteur) Large-scale bark beetle outbreaks in spruce dominated mountain forests have increased in recent decades, and this trend is expected to continue in the future. These outbreaks have immediate and major effects on forest structure and ecosystem services. However, it remains unclear how forests recover from bark beetle infestations over the long term, and how different recovery stages fulfil the capacity of forests to protect infrastructures and human lives from natural hazards. The aim of this study was to investigate how a bark beetle infestation (1992–1997) in a spruce dominated forest in the Swiss Alps changed the forest structure and its protective function against snow avalanches. In 2020, i.e. 27 years after the peak of the outbreak, we re-surveyed the composition and height of new trees, as well as the deadwood height and degree of decay in an area that had been surveyed 20 years earlier. With the help of remote sensing data and avalanche simulations, we assessed the protective effect against avalanches before the disturbances (in 1985) and in 1997, 2007, 2014 and 2019 for a frequent (30-year return period) and an extreme (300-year return period) avalanche scenario. Post-disturbance regeneration led to a young forest that was again dominated by spruce 27 years after the outbreak, with median tree heights of 3–4 m and a crown cover of 10–30%. Deadwood covered 20–25% of the forest floor and was mainly in decay stages two and three out of five. Snags had median heights of 1.4 m, leaning logs 0.5 m and lying logs 0.3 m. The protective effect of the forest was high before the bark beetle outbreak and decreased during the first years of infestation (until 1997), mainly in the case of extreme avalanche events. The protective capacity reached an overall minimum in 2007 as a result of many forest openings. It partially recovered by 2014 and further increased by 2019, thanks to forest regeneration. Simulation results and a lack of avalanche releases since the infestation indicate that the protective capacity of post-disturbance forest stands affected by bark beetle may often be underestimated. Numéro de notice : A2022-349 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.foreco.2022.120201 Date de publication en ligne : 08/04/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120201 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100536 [article]

ART-RISK 3.0, a fuzzy-based platform that combine GIS and expert assessments for conservation strategies in cultural heritage / M. Moreno in Journal of Cultural Heritage, vol 55 (May - June 2022)  

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Landslide susceptibility assessment considering spatial agglomeration and dispersion characteristics: A case study of Bijie City in Guizhou Province, China / Kezhen Yao in ISPRS International journal of geo-information, vol 11 n° 5 (May 2022)  

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Deep mass redistribution prior to the 2010 Mw 8.8 Maule (Chile) Earthquake revealed by GRACE satellite gravity / Marie Bouih in Earth and planetary science letters, vol 584 (15 April 2022)  

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Determination of building flood risk maps from LiDAR mobile mapping data / Yu Feng in Computers, Environment and Urban Systems, vol 93 (April 2022)  

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Flood mapping using multi-temporal Sentinel-1 SAR images: A case study—Inaouene watershed from Northeast of Morocco / Brahim Benzougagh in Iranian Journal of Science and Technology - Transactions of Civil Engineering, vol 46 n° 2 (April 2022)  

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Natural disturbances risks in European boreal and temperate forests and their links to climate change : A review of modelling approaches / Joyce Machado Nunes Romeiro in Forest ecology and management, vol 509 (April-1 2022)  

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Mapping burn severity in the western Italian Alps through phenologically coherent reflectance composites derived from Sentinel-2 imagery / Donato Morresi in Remote sensing of environment, vol 269 (February 2022)  

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Multi-method monitoring of rockfall activity along the classic route up Mont Blanc (4809 m a.s.l.) to encourage adaptation by mountaineers / Jacques Mourey in Natural Hazards and Earth System Sciences, vol 22 n° 2 (February 2022)  

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Atlas of global change risk of population and economic systems / Peijun Shi (2022)  

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Combining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China / Huijuan Zhang in Computers & geosciences, vol 158 (January 2022)  

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