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Comparison of landslide susceptibility mapping based on statistical index, certainty factors, weights of evidence and evidential belief function models / Kai Cui in Geocarto international, vol 32 n° 9 (September 2017)
[article]
Titre : Comparison of landslide susceptibility mapping based on statistical index, certainty factors, weights of evidence and evidential belief function models Type de document : Article/Communication Auteurs : Kai Cui, Auteur ; Dong Lu, Auteur ; Wei Li, Auteur Année de publication : 2017 Article en page(s) : pp 935 - 955 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] aléa
[Termes IGN] analyse comparative
[Termes IGN] cartographie des risques
[Termes IGN] Chine
[Termes IGN] effondrement de terrain
[Termes IGN] inventaire
[Termes IGN] pondération
[Termes IGN] risque naturel
[Termes IGN] système d'information géographique
[Termes IGN] théorie de Dempster-Shafer
[Termes IGN] vulnérabilitéRésumé : (Auteur) The main aim of this study was to produce landslide susceptibility maps using statistical index (SI), certainty factors (CF), weights of evidence (WoE) and evidential belief function (EBF) models for the Long County, China. Firstly, a landslide inventory map, including a total of 171 landslides, was compiled on the basis of earlier reports, interpretation of aerial photographs and supported by extensive field surveys. Thereafter, all landslides were randomly separated into two data sets: 70% landslides (120 points) were selected for establishing the model and the remaining landslides (51 points) were used for validation purposes. Eleven landslide conditioning factors, such as slope aspect, slope angle, plan curvature, profile curvature, altitude, distance to faults, distance to roads, distance to rivers, lithology, NDVI and land use, were considered for landslide susceptibility mapping in this study. Then, the SI, CF, WoE and EBF models were used to produce the landslide susceptibility maps for the study area. Finally, the four models were validated using area under the curve (AUC) method. According to the validation results, the EBF model (AUC = 78.93%) has a higher prediction accuracy than the SI model (AUC = 77.72%), the WoE model (AUC = 77.62%) and the CF model (AUC = 77.72%). Similarly, the validation results also indicate that the EBF model has the highest training accuracy of 80.25%, followed by SI (79.80%), WoE (79.71%) and CF (79.67%) models. Numéro de notice : A2017-457 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.0/10106049.2016.1195886 Date de publication en ligne : 16/06/2016 En ligne : http://dx.doi.org108/10.0/10106049.2016.1195886 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86382
in Geocarto international > vol 32 n° 9 (September 2017) . - pp 935 - 955[article]Investigation of automatic feature weighting methods (Fisher, Chi-square and Relief-F) for landslide susceptibility mapping / Emrehan Kutlug Sahin in Geocarto international, vol 32 n° 9 (September 2017)
[article]
Titre : Investigation of automatic feature weighting methods (Fisher, Chi-square and Relief-F) for landslide susceptibility mapping Type de document : Article/Communication Auteurs : Emrehan Kutlug Sahin, Auteur ; Cengizhan Ipbuker, Auteur ; Taskin Kavzoglu, Auteur Année de publication : 2017 Article en page(s) : pp 956 - 977 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] analyse comparative
[Termes IGN] cartographie des risques
[Termes IGN] distribution de Fisher
[Termes IGN] effondrement de terrain
[Termes IGN] khi carré
[Termes IGN] pondération
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] risque naturel
[Termes IGN] surveillance géologique
[Termes IGN] test de performance
[Termes IGN] vulnérabilitéRésumé : (Auteur) In landslide susceptibility mapping, factor weights have been usually determined by expert judgements. A novel methodology for weighting landslide causative factors by integrating statistical feature weighting algorithms was proposed. The primary focus of this study is to investigate the effectiveness of automatic feature weighting algorithms, namely Fisher, Chi-square and Relief-F algorithms. Analytic hierarchy process (AHP) method was used as a benchmark method to compare the performances of the weighting algorithms. All weighted factors were tested using factor-weighted overlay method, and quality of these maps was assessed using overall accuracy, area under the ROC curve (AUC) and success rate curve. In addition, Wilcoxon’s signed-rank test was applied to evaluate statistical differences between both estimated overall accuracies and AUCs, respectively. Results showed that the weights determined by feature weighting methods outperformed the conventional AHP method by about 6% and this level of differences was found to be statistically significant. Numéro de notice : A2017-458 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1170892 Date de publication en ligne : 11/04/2016 En ligne : http://dx.doi.org/10.1080/10106049.2016.1170892 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86383
in Geocarto international > vol 32 n° 9 (September 2017) . - pp 956 - 977[article]Ne plus négliger le recul des falaises méditerranéennes / Marielle Mayo in Géomètre, n° 2149 (juillet - août 2017)
[article]
Titre : Ne plus négliger le recul des falaises méditerranéennes Type de document : Article/Communication Auteurs : Marielle Mayo, Auteur Année de publication : 2017 Article en page(s) : pp 46 - 49 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse diachronique
[Termes IGN] Bureau de recherches géologiques et minières
[Termes IGN] données lidar
[Termes IGN] effondrement de terrain
[Termes IGN] gestion des risques
[Termes IGN] littoral méditerranéen
[Termes IGN] orthoimage
[Termes IGN] précision centimétrique
[Termes IGN] surveillance du littoralRésumé : (auteur) Minimisé par des habitants trop confiants, le risque lié à l'érosion a été confirmé par le projet Valse, qui a mis en lumière le grignotage de la côte depuis des millénaires. Numéro de notice : A2017-393 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85908
in Géomètre > n° 2149 (juillet - août 2017) . - pp 46 - 49[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 063-2017071 RAB Revue Centre de documentation En réserve L003 Disponible A simple but effective landslide detection method based on image saliency / Bo Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 5 (May 2017)
[article]
Titre : A simple but effective landslide detection method based on image saliency Type de document : Article/Communication Auteurs : Bo Yu, Auteur ; Fang Chen, Auteur ; Muhammad Shakir, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 351 - 363 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] détection de changement
[Termes IGN] effondrement de terrain
[Termes IGN] extraction du relief
[Termes IGN] relief
[Termes IGN] risque naturelRésumé : (auteur) Effective large-scale landslide mapping is becoming significantly important for analyzing natural hazards and providing landslide locations rapidly for emergency response. Change detection and machine learning methods are commonly used for landslide detection. Change detection mostly relies on several experienced parameters that users have to tune for different images, which limits the practical application. The training machine learning model consumes much time, and it is limited to specific imaging conditions. In this paper, a simple method for landslide detection using a fixed parameter by calculating image saliency is proposed. Landslide is detected as a saliency object within the background of vegetation and bare rocks. It is fast and robust for the experimental images, and outperforms the state-of-the-art, semi-automatic method in terms of accuracy and computing time. Given the high efficiency and robustness of the proposed method, it is applicable to practical cases for hazard estimation. Numéro de notice : A2017-190 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.5.351 En ligne : https://doi.org/10.14358/PERS.83.5.351 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84800
in Photogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 5 (May 2017) . - pp 351 - 363[article]A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping / Wei Chen in Geocarto international, vol 32 n° 4 (April 2017)
[article]
Titre : A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping Type de document : Article/Communication Auteurs : Wei Chen, Auteur ; Hamid Reza Pourghasemi, Auteur ; Zhou Zhao, Auteur Année de publication : 2017 Article en page(s) : pp 367 - 385 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] aléa
[Termes IGN] analyse comparative
[Termes IGN] ArcGIS
[Termes IGN] cartographie des risques
[Termes IGN] Chine
[Termes IGN] classification de Dempster-Shafer
[Termes IGN] classification par réseau neuronal
[Termes IGN] effondrement de terrain
[Termes IGN] régression logistique
[Termes IGN] risque naturel
[Termes IGN] vulnérabilitéRésumé : (Auteur) The main aim of present study is to compare three GIS-based models, namely Dempster–Shafer (DS), logistic regression (LR) and artificial neural network (ANN) models for landslide susceptibility mapping in the Shangzhou District of Shangluo City, Shaanxi Province, China. At First, landslide locations were identified by aerial photographs and supported by field surveys, and a total of 145 landslide locations were mapped in the study area. Subsequently, the landslide inventory was randomly divided into two parts (70/30) using Hawths Tools in ArcGIS 10.0 for training and validation purposes, respectively. In the present study, 14 landslide conditioning factors such as altitude, slope angle, slope aspect, topographic wetness index, sediment transport index, stream power index, plan curvature, profile curvature, lithology, rainfall, distance to rivers, distance to roads, distance to faults and normalized different vegetation index were used to detect the most susceptible areas. In the next step, landslide susceptible areas were mapped using the DS, LR and ANN models based on landslide conditioning factors. Finally, the accuracies of the landslide susceptibility maps produced from the three models were verified using the area under the curve (AUC). The validation results showed that the landslide susceptibility map generated by the ANN model has the highest training accuracy (73.19%), followed by the LR model (71.37%), and the DS model (66.42%). Similarly, the AUC plot for prediction accuracy presents that ANN model has the highest accuracy (69.62%), followed by the LR model (68.94%), and the DS model (61.39%). According to the validation results of the AUC curves, the map produced by these models exhibits the satisfactory properties. Numéro de notice : A2017-271 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1140824 Date de publication en ligne : 22/03/2016 En ligne : http://doi.org/10.1080/10106049.2016.1140824 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85297
in Geocarto international > vol 32 n° 4 (April 2017) . - pp 367 - 385[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2017041 RAB Revue Centre de documentation En réserve L003 Disponible Geodetic monitoring of subrosion-induced subsidence processes in urban areas / Tobias Kersten in Journal of applied geodesy, vol 11 n° 1 (March 2017)PermalinkPermalinkThe protective effect of forests against rockfalls across the French Alps: Influence of forest diversity / S. Dupire in Forest ecology and management, vol 382 (15 December 2016)PermalinkStructure and deformation of the Southern Taiwan accretionary prism: The active submarine Fangliao Fault Zone offshore west Hengchun Peninsula / Benoit Deffontaines in Tectonophysics, vol 692 part B (5 December 2016)PermalinkPrise en compte des forêts à fonction de protection dans les cartographies réglementaires de prévention des risques naturels : Tour d’horizon européen et recommandations pour la France / Jérôme Liévois in Rendez-vous techniques, n° 51-52 (printemps - été 2016)PermalinkMeasurement of surface changes in a scaled-down landslide model using high-speed stereo image sequences / Tiantian Feng in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 7 (juillet 2016)PermalinkRemote sensing of alpine glaciers in visible and infrared wavelengths: a survey of advances and prospects / Anshuman Bhardwaj in Geocarto international, vol 31 n° 5 - 6 (May - June 2016)PermalinkData fusion technique using wavelet transform and Taguchi methods for automatic landslide detection from airborne laser scanning data and QuickBird satellite imagery / Biswajeet Pradhan in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)PermalinkUn modèle global d'anticipation des mouvements de terrain / Anonyme in GEOrama, le journal d'Information du BRGM, n° 32 (mars 2016)PermalinkReal time monitoring ground motion using GPS with real time corrections / R. Tu in Survey review, vol 48 n° 347 (March 2016)Permalink