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Three-dimensional simulations of rockfalls in Ischia, Southern Italy, and preliminary susceptibility zonation / Massimiliano Alvioli in Geomatics, Natural Hazards and Risk, vol 13 (2022)
[article]
Titre : Three-dimensional simulations of rockfalls in Ischia, Southern Italy, and preliminary susceptibility zonation Type de document : Article/Communication Auteurs : Massimiliano Alvioli, Auteur ; Ada De Mateo, Auteur ; Raffaele Castaldo, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2712 - 2736 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] cartographie des risques
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] éboulement
[Termes IGN] île
[Termes IGN] Italie
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de terrain
[Termes IGN] pente
[Termes IGN] risque naturel
[Termes IGN] roche
[Termes IGN] séisme
[Termes IGN] semis de points
[Termes IGN] surveillance géologique
[Termes IGN] vulnérabilitéRésumé : (auteur) Ischia Island is a volcano-tectonic horst in the Phlegrean Volcanic District, Italy. We investigated rockfalls in Ischia using STONE, a three-dimensional model for simulating trajectories for given detachment locations of blocks. We propose methodological advances regarding the use of high-resolution LiDAR elevation data, the localization of possible detachments sources, and the inclusion of scenario-based seismic shaking as a trigger for rockfalls. We demonstrated that raw LiDAR data are useful to distinguish areas covered by tall vegetation, allowing realistic simulation of trajectories. We found that the areas most susceptibile to rockfalls are located along the N, N-W and S-W steep flanks of Mt. Epomeo, the S and S-W coast, and the sides of some steep exposed hydrographic channels located in the southern sector of the island. A novel procedure for dynamic activation of sources depending on ground shaking, in the event of an earthquake, helped inferring a seismically-triggered source map and the corresponding rockfall trajectories, for a scenario with 475 y return time. Thus, we obtained preliminary rockfall suceptibility in Ischia both in a “static” (trigger-independent) scenario, and in a seismic shaking triggering scenario. They must not be considered a risk map, but a starting point for a detailed field analysis. Numéro de notice : A2022-874 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : https://doi.org/10.1080/19475705.2022.2131472 Date de publication en ligne : 09/10/2022 En ligne : https://doi.org/10.1080/19475705.2022.2131472 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102172
in Geomatics, Natural Hazards and Risk > vol 13 (2022) . - pp 2712 - 2736[article]Towards urban flood susceptibility mapping using data-driven models in Berlin, Germany / Omar Seleem in Geomatics, Natural Hazards and Risk, vol 13 (2022)
[article]
Titre : Towards urban flood susceptibility mapping using data-driven models in Berlin, Germany Type de document : Article/Communication Auteurs : Omar Seleem, Auteur ; Georgy Ayzel, Auteur Année de publication : 2022 Article en page(s) : pp 1640 - 1662 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] Berlin
[Termes IGN] cartographie des risques
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] inondation
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] vulnérabilitéRésumé : (auteur) Identifying urban pluvial flood-prone areas is necessary but the application of two-dimensional hydrodynamic models is limited to small areas. Data-driven models have been showing their ability to map flood susceptibility but their application in urban pluvial flooding is still rare. A flood inventory (4333 flooded locations) and 11 factors which potentially indicate an increased hazard for pluvial flooding were used to implement convolutional neural network (CNN), artificial neural network (ANN), random forest (RF) and support vector machine (SVM) to: (1) Map flood susceptibility in Berlin at 30, 10, 5, and 2 m spatial resolutions. (2) Evaluate the trained models' transferability in space. (3) Estimate the most useful factors for flood susceptibility mapping. The models' performance was validated using the Kappa, and the area under the receiver operating characteristic curve (AUC). The results indicated that all models perform very well (minimum AUC = 0.87 for the testing dataset). The RF models outperformed all other models at all spatial resolutions and the RF model at 2 m spatial resolution was superior for the present flood inventory and predictor variables. The majority of the models had a moderate performance for predictions outside the training area based on Kappa evaluation (minimum AUC = 0.8). Aspect and altitude were the most influencing factors on the image-based and point-based models respectively. Data-driven models can be a reliable tool for urban pluvial flood susceptibility mapping wherever a reliable flood inventory is available. Numéro de notice : A2022-457 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1080/19475705.2022.2097131 Date de publication en ligne : 12/07/2022 En ligne : https://doi.org/10.1080/19475705.2022.2097131 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101257
in Geomatics, Natural Hazards and Risk > vol 13 (2022) . - pp 1640 - 1662[article]A comparative approach of support vector machine kernel functions for GIS-based landslide susceptibility mapping / Khalil Valizadeh Kamran in Applied geomatics, vol 13 n° 4 (December 2021)
[article]
Titre : A comparative approach of support vector machine kernel functions for GIS-based landslide susceptibility mapping Type de document : Article/Communication Auteurs : Khalil Valizadeh Kamran, Auteur ; Bakhtiar Feizizadeh, Auteur ; Behnam Khorrami, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 837 - 851 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de sensibilité
[Termes IGN] apprentissage automatique
[Termes IGN] cartographie des risques
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] effondrement de terrain
[Termes IGN] fonction de base radiale
[Termes IGN] Iran
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] occupation du sol
[Termes IGN] pente
[Termes IGN] risque naturel
[Termes IGN] système d'information géographique
[Termes IGN] utilisation du solRésumé : (auteur) Landslides are among the most destructive natural hazards with severe socio-economic ramifications all around the world. Understanding the critical combination of geoenvironmental factors involved in the occurrence of landslides can mitigate the adverse impacts ascribed to them. Among the several scenarios for studying and investigating this phenomenon, landslide susceptibility mapping (LSM) is the most prominent method. Applying the machine learning (ML) algorithms integrated with the geographic information systems (GIS) has become a trending means for accurate and rapid landslide mapping practices in the scientific community. Support vector machine (SVM) has been the most commonly applied ML algorithm for LSM in recent years. The current study aims to implement different SVM kernel functions including polynomial kernel function (PKF) (degree 1 to 5), radial basis function (RBF), sigmoid, and linear kernels, for a GIS-based LSM over the Tabriz Basin (TB). To this end, a total number of 9 conditioning parameters being involved in the occurrence of the landslide events were determined and utilized. The LSM maps of the TB were generated based on the different SVM kernels and were statistically validated according to the landslide inventory. The findings revealed that the polynomial-degree-2 (PKF-2) model (AUC = 0.9688) outperforms the rest of the utilized kernels. According to the SLM map generated through PKF-2, the northernmost parts of the TB are extremely susceptible to slope failures than the rest; therefore, the developmental policies over these parts have to be taken into account with privileged priority to hinder any humanitarian as well as environmental catastrophes. Numéro de notice : A2021-858 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s12518-021-00393-0 Date de publication en ligne : 28/08/2021 En ligne : https://doi.org/10.1007/s12518-021-00393-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99066
in Applied geomatics > vol 13 n° 4 (December 2021) . - pp 837 - 851[article]A GIS-remote sensing approach for forest fire risk assessment: case of Bizerte region, Tunisia / Salwa Saidi in Applied geomatics, vol 13 n° 4 (December 2021)
[article]
Titre : A GIS-remote sensing approach for forest fire risk assessment: case of Bizerte region, Tunisia Type de document : Article/Communication Auteurs : Salwa Saidi, Auteur ; Alaeddine Ben Younes, Auteur ; Brice Anselme, Auteur Année de publication : 2021 Article en page(s) : pp 587–603 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] analyse multicritère
[Termes IGN] Bizerte (Tunisie)
[Termes IGN] cartographie des risques
[Termes IGN] changement climatique
[Termes IGN] incendie de forêt
[Termes IGN] indice de risque
[Termes IGN] outil d'aide à la décision
[Termes IGN] prévention des risques
[Termes IGN] système d'information géographiqueRésumé : (auteur) In this era of climate change and global warming, forest fires are increasing around the world and especially in areas with arid and semi-arid climate. Hence, prevention is vital and it is considered as the best solution to protect forest areas. This paper presents a multi-criteria approach for the assessment and mapping of fire risk using three indicators: topomorphology index, climatic index, and human one. For each indicator, sub-indicators such as slope, morphology, exposure, number of fires, groundwater reserve, and evapotranspiration are chosen to generate a forest fire risk index in Bizerte region. Spatial data on all these indicators have been aggregated and organized in a geographic information system (GIS) framework. Results show that 33% of the total area of Bizerte forest is highly vulnerable to fire risk and an increasing of risk from 2013 to 2016. Sensitivity analyses indicated that the removal of the climatic (ICL) and the human indexes (HI) from the forest fire risk index causes large variation in the risk assessment. As a consequence, it should have higher weights than other indicators, which proves that triggering of wildfires is in the whole part caused by human activities and accelerated by climatic conditions. The remote sensing approach using NBR index confirms that severity of burned area increases throughout the time and the most changes are observed in the Northeast of Bizerte forest. These results can serve as a planning tool for decision makers to save the lives of residents and forest resources. Numéro de notice : A2021-857 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article DOI : 10.1007/s12518-021-00369-0 Date de publication en ligne : 03/06/2021 En ligne : https://doi.org/10.1007/s12518-021-00369-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99065
in Applied geomatics > vol 13 n° 4 (December 2021) . - pp 587–603[article]GIS to identify exposed shoreline sectors to wave impacts: case of El Tarf coast / Abdeldjalil Goumrasa in Applied geomatics, vol 13 n° 4 (December 2021)
[article]
Titre : GIS to identify exposed shoreline sectors to wave impacts: case of El Tarf coast Type de document : Article/Communication Auteurs : Abdeldjalil Goumrasa, Auteur ; Chawki Zerrouki, Auteur ; Yacine Hemdane, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 493 - 498 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] Algérie
[Termes IGN] cartographie des risques
[Termes IGN] érosion côtière
[Termes IGN] géomorphologie
[Termes IGN] houle
[Termes IGN] modèle numérique de surface
[Termes IGN] risque naturel
[Termes IGN] submersion marine
[Termes IGN] surveillance du littoral
[Termes IGN] trait de côte
[Termes IGN] urbanisation
[Termes IGN] vagueRésumé : (auteur) Many coastlines are naturally exposed to submersion and erosion hazards. In this work, a combined GIS and wave modeling method is used to identify areas exposed to coastal erosion. The main objective of this work is the qualitative identification of shoreline sectors where wave and swell energy are important. The selected study area is “Cap Rosa” located in the El Tarf coast in eastern Algeria. The obtained results show that the eastern part of the study area is the most affected by wave impact. This preliminary identification can be used in a detailed vulnerability assessment along the coast while integrating other factors (physical and socio-economic) beside the shoreline exposure. Numéro de notice : A2021-855 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s12518-021-00380-5 Date de publication en ligne : 24/05/2021 En ligne : https://doi.org/10.1007/s12518-021-00380-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99061
in Applied geomatics > vol 13 n° 4 (December 2021) . - pp 493 - 498[article]Incorporating multi-criteria decision-making and fuzzy-value functions for flood susceptibility assessment / Ali Azareh in Geocarto international, vol 36 n° 20 ([01/12/2021])PermalinkEvaluation of watershed soil erosion hazard using combination weight and GIS: a case study from eroded soil in Southern China / Shifa Chen in Natural Hazards, vol 109 n° 2 (November 2021)PermalinkPotential flood hazard zone mapping based on geomorphologic considerations and fuzzy analytical hierarchy model in a data scarce West African basin / Olabanji Aladejana in Geocarto international, vol 36 n° 19 ([01/11/2021])PermalinkTidal flood area mapping in the face of climate change scenarios: case study in a tropical estuary in the Brazilian semi-arid region / Paulo Victor N. Araújo in Natural Hazards and Earth System Sciences, vol 21 n° 11 (November 2021)PermalinkFlood inundation mapping and hazard assessment of Baitarani River basin using hydrologic and hydraulic model / Gaurav Talukdar in Natural Hazards, vol 109 n° 1 (October 2021)PermalinkPrioritization of forest fire hazard risk simulation using Hybrid Grey Relativity Analysis (HGRA) and Fuzzy Analytical Hierarchy Process (FAHP) coupled with multicriteria decision analysis (MCDA) techniques – a comparative study analysis / Michael Stanley Peprah in Geodesy and cartography, vol 47 n° 3 (October 2021)PermalinkProtection naturelle contre la submersion, apport de l'intelligence artificielle / Antoine Mury in Cartes & Géomatique, n° 245-246 (septembre - décembre 2021)PermalinkRapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning / Xin Jiang in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)PermalinkSpatio-temporal-spectral observation model for urban remote sensing / Zhenfeng Shao in Geo-spatial Information Science, vol 24 n° 3 (July 2021)PermalinkFlood risk mapping using uncertainty propagation analysis on a peak discharge: case study of the Mille Iles River in Quebec / Jean-Marie Zokagoa in Natural Hazards, vol 107 n° 1 (May 2021)Permalink