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Termes IGN > géomatique > système d'information géographique
système d'information géographiqueSynonyme(s)Système d'information sur le territoire ;système d'information localisée ;système d'information à référence spatiale ;SIRS ;SIG gisVoir aussi |
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Developing a GIS-based rough fuzzy set granulation model to handle spatial uncertainty for hydrocarbon structure classification, case study: Fars domain, Iran / Sahand Seraj in Geo-spatial Information Science, vol 25 n° 3 (October 2022)
[article]
Titre : Developing a GIS-based rough fuzzy set granulation model to handle spatial uncertainty for hydrocarbon structure classification, case study: Fars domain, Iran Type de document : Article/Communication Auteurs : Sahand Seraj, Auteur ; Mahmoud Reza Delavar, Auteur Année de publication : 2022 Article en page(s) : pp 399 - 41 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] cartographie géologique
[Termes IGN] classification floue
[Termes IGN] entropie de Shannon
[Termes IGN] forage
[Termes IGN] granulométrie (pétrologie)
[Termes IGN] hydrocarbure
[Termes IGN] incertitude géométrique
[Termes IGN] Iran
[Termes IGN] prospection minérale
[Termes IGN] sous ensemble flou
[Termes IGN] système d'information géographiqueRésumé : (auteur) It is well agreed that geologic risk occurs during hydrocarbon exploration because diverse uncertainties accompany the entire hydrocarbon system parameters such as the source rock, reservoir rock, trap and seal rock. In order to overcome such attributes with uncertainties, a number of soft computing methods are used. Information granules could be provided by the Rough Fuzzy Set Granulation (RFSG) with a thorough quality evaluation. This is capable of attribute reduction that has been claimed to be essential in investigating the hydrocarbon systems. This paper is an endeavor to recommend a Geospatial Information System (GIS)-based model with the aim of categorizing the hydrocarbon structures map consistent with the uncertainty range concepts of geologic risk in the rough fuzzy sets and granular computing. The model used the RFSG for the attribute reduction by a Decision Logic language (DL-language). The RFSG was employed in order to classify hydrocarbon structures according to geological risk and extract the fuzzy rules with a predefined range of uncertainty. In order to assess the precisions of the fuzzy decisions on the hydrocarbon structure classification, the fuzzy entropy and fuzzy cross-entropy are applied. The proposed RFSG model applied for 62 structures as the training data, average fuzzy entropy has been calculated as 0.85, whereas the average fuzzy cross-entropy has been calculated 0.18. As it can be discerned, just seven structures had cross-entropies greater than 0.1, while three structures were larger than 0.3. It is implied that the precision of the proposed model is about 89%. The results yielded two reductions for the condition attributes and 11 fuzzy rules being filtered by the granular computing values. Numéro de notice : A2022-724 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10095020.2021.2020600 Date de publication en ligne : 03/02/2022 En ligne : https://doi.org/10.1080/10095020.2021.2020600 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101667
in Geo-spatial Information Science > vol 25 n° 3 (October 2022) . - pp 399 - 41[article]Remote sensing and GIS based Soil Loss Estimation for Bhutan, using RUSLE model / Sangay Gyeltshen in Geocarto international, Vol 37 n° 21 ([01/10/2022])
[article]
Titre : Remote sensing and GIS based Soil Loss Estimation for Bhutan, using RUSLE model Type de document : Article/Communication Auteurs : Sangay Gyeltshen, Auteur ; Rabindra Adhikarib, Auteur ; Padam Bahadur Budha, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 6331 - 6350 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] Bhoutan
[Termes IGN] distribution spatiale
[Termes IGN] effondrement de terrain
[Termes IGN] érosion
[Termes IGN] modèle RUSLE
[Termes IGN] système d'information géographique
[Termes IGN] télédétection
[Termes IGN] utilisation du solRésumé : (auteur) The repository of soil by water at a national and basin scale was estimated using the RUSLE empirical model which is the first of its kind in Bhutan. The annual soil loss is estimated and categorized into five categories: very low (800 t/yr). Sakteng and Jaldakha basins contributed the highest soil loss rate of 0.04 and 0.039 t/ha/yr, while considering on landuse pattern, non-built-up and landslide category encountered the highest soil loss of 4.09 and 0.7 t/ha/yr among others. Similarly, Tsirang, Samtse and Haa contributed the major soil loss of 0.03, 0.0298 and 0.02 t/ha/yr, respectively. The research can be used as an authentic instrument enabling the soil conservationist and the policymakers to evaluate the adverse impacts, prioritize the conservation efforts and investigate further to narrow down the causes of soil erosion. Numéro de notice : A2022-718 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.1080/10106049.2021.1936210 Date de publication en ligne : 22/06/2021 En ligne : https://doi.org/10.1080/10106049.2021.1936210 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101646
in Geocarto international > Vol 37 n° 21 [01/10/2022] . - pp 6331 - 6350[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2022211 RAB Revue Centre de documentation En réserve L003 Disponible Assessing road accidents in spatial context via statistical and non-statistical approaches to detect road accident hotspot using GIS / Yegane Khosravi in Geodetski vestnik, vol 66 n° 3 (September - November 2022)
[article]
Titre : Assessing road accidents in spatial context via statistical and non-statistical approaches to detect road accident hotspot using GIS Type de document : Article/Communication Auteurs : Yegane Khosravi, Auteur ; Farhad Hosseinali, Auteur ; Mostafa Adresi, Auteur Année de publication : 2022 Article en page(s) : pp 412 - 431 Note générale : bibliographie Langues : Anglais (eng) Slovène (slv) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] accident de la route
[Termes IGN] analyse de groupement
[Termes IGN] autocorrélation spatiale
[Termes IGN] classification par nuées dynamiques
[Termes IGN] corrélation automatique de points homologues
[Termes IGN] distance de Manhattan
[Termes IGN] estimation par noyau
[Termes IGN] Iran
[Termes IGN] méthode statistique
[Termes IGN] pente
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] regroupement de données
[Termes IGN] système d'information géographiqueRésumé : (auteur) Road accidents are among the most critical causes of fatality, personal injuries, and financial damage worldwide. Identifying accident hotspots and the causes of accidents and improving the condition of these hotspots is an economical way to improve road traffic safety. In this study, to identify the accident hotspots of “Dehbala” road located in Yazd province-Iran, statistical and non-statistical clustering methods were used. First, the weighting of the criteria was performed by an expert using the AHP method. Hence, the spatial correlation of slope and curvature was calculated by Global Moran’I. Anselin Local Moran index and Getis-Ord Gi* and Kernel Density Estimation were used to identify accident hotspots based on accident location due to the density of points. As a result, four accident hotspots were obtained by the Anselin Local Moran index, three accident hotspots by Getis-Ord Gi*and one accident-prone area were obtained by Kernel Density Estimation method. Three algorithms, k-means, k-medoids, and DBSCAN, were used to identify accident-prone areas or points using non-statistical methods. The dense cluster of each method was considered as an accident-prone cluster. Then the results of statistical and non- statistical methods were intersected with each other and the final accident-prone area was obtained. This study revealed the effect of geometric charcateristics of the road (slope and curvature) on the occurance of accidents. Numéro de notice : A2022-781 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.15292/geodetski-vestnik.2022.03.412-431 Date de publication en ligne : 04/08/2022 En ligne : https://doi.org/10.15292/geodetski-vestnik.2022.03.412-431 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101864
in Geodetski vestnik > vol 66 n° 3 (September - November 2022) . - pp 412 - 431[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 139-2022031 RAB Revue Centre de documentation En réserve L003 Disponible 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)
[article]
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 denseRésumé : (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
in Natural Hazards > vol 113 n° 2 (September 2022) . - pp 1043 - 1081[article]"Process toponymy": A GIS-based community-engaged approach to indigenous dynamic place naming systems and vernacular cartography / Nadezhda Mamontova in Cartographica, vol 57 n° 3 (September 2022)
[article]
Titre : "Process toponymy": A GIS-based community-engaged approach to indigenous dynamic place naming systems and vernacular cartography Type de document : Article/Communication Auteurs : Nadezhda Mamontova, Auteur ; Elena Klyachko, Auteur Année de publication : 2022 Article en page(s) : pp 213 - 225 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Toponymie
[Termes IGN] carte thématique
[Termes IGN] ontologie
[Termes IGN] portail
[Termes IGN] sémiologie graphique
[Termes IGN] Sibérie
[Termes IGN] système d'information géographique
[Termes IGN] toponymie localeRésumé : (auteur) This paper discusses the aim and the process of designing a community-engaged open-access GIS toponymic platform, based on Indigenous Evenki place names. Most projects on Indigenous toponymy available online are either oriented towards professional use among scholars or serve as enclosed repositories of Indigenous knowledge. Toponymic atlases remain the most common form of documenting and representing Indigenous place naming systems. Yet, temporal and geographic comparisons of place names have clearly demonstrated that, along with a conventional understanding of Indigenous place names as stable and conservative, there is a dynamic model of place naming to be found in nomadic societies, when the names are not only passed through generations but also modified and created. This finding required a number of methodological approaches regarding how researchers might collect and represent geospatial concepts and place names in nomadic societies, with the use of GIS technology. Our project attempts to approach this issue by creating an open digital platform that combines GIS with Indigenous vernacular cartography, place names, and a great variety of data regarding the meaning and use of toponyms, their evolution, and change. We call this approach a “process toponymy” and advocate for applying a semiotic approach to documenting and representing Indigenous place names’ knowledge via GIS-based platforms. Numéro de notice : A2022-848 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3138/cart-2022-0010 Date de publication en ligne : 02/11/2022 En ligne : https://doi.org/10.3138/cart-2022-0010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102086
in Cartographica > vol 57 n° 3 (September 2022) . - pp 213 - 225[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 031-2022031 RAB Revue Centre de documentation En réserve L003 Disponible Study on city digital twin technologies for sustainable smart city design: A review and bibliometric analysis of geographic information system and building information modeling integration / Haishan Xia in Sustainable Cities and Society, vol 84 (September 2022)PermalinkEvapotranspiration mapping of cotton fields in Brazil: comparison between SEBAL and FAO-56 method / Juan Vicente Liendro Moncada in Geocarto international, Vol 37 n° 17 ([20/08/2022])PermalinkA model development on GIS-driven data to predict temporal daily collision through integrating Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms; case study: Tehran-Qazvin freeway / Reza Sanayeia in Geocarto international, vol 37 n° 14 ([20/07/2022])PermalinkGANmapper: geographical data translation / Abraham Noah Wu in International journal of geographical information science IJGIS, vol 36 n° 7 (juillet 2022)PermalinkModelling areas for sustainable forest management in a mining and human dominated landscape: A Geographical Information System (GIS)- Multi-Criteria Decision Analysis (MCDA) approach / Xavier Takam Tiamgne in Annals of GIS, vol 28 n° 3 (July 2022)PermalinkAnalysis of the land suitability for paddy fields in Tanzania using a GIS-based analytical hierarchy process / Ahmad Al-Hanbali in Geo-spatial Information Science, vol 25 n° 2 ([01/06/2022])PermalinkEfficient calculation of distance transform on discrete global grid systems / Meysam Kazemi in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)PermalinkA geospatial workflow for the assessment of public transit system performance using near real-time data / Anastassios Dardas in Transactions in GIS, vol 26 n° 4 (June 2022)PermalinkGIS and machine learning for analysing influencing factors of bushfires using 40-year spatio-temporal bushfire data / Wanqin He in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)PermalinkGIS-based assessment of long-term traffic accidents using spatiotemporal and empirical Bayes analysis in Turkey / Saffet Erdoğan in Applied geomatics, vol 14 n° 2 (June 2022)PermalinkNarrative cartography with knowledge graphs / Gengchen Mai in Journal of Geovisualization and Spatial Analysis, vol 6 n° 1 (June 2022)PermalinkPhysical modelling of Nanda Devi National Park, a natural world heritage site, from GIS data / Sanat Agrawal in Cartographica, vol 57 n° 2 (Summer 2022)PermalinkExploring digital twin adaptation to the urban environment: comparison with CIM to avoid silo-based approaches / Adeline Deprêtre in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2022 (2022 edition)PermalinkART-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)PermalinkA GIS representation framework for location-based social media activities / Xuebin Wei in Transactions in GIS, vol 26 n° 3 (May 2022)PermalinkAssessment of land suitability potentials for winter wheat cultivation by using a multi criteria decision Support-Geographic information system (MCDS-GIS) approach in Al-Yarmouk Basin (Syria) / Safwan Mohammed in Geocarto international, vol 37 n° 6 ([01/04/2022])PermalinkLa bathymétrie ancienne au service de l’étude de tsunamis inexpliqués : le cas du pertuis d’Antioche (1785, 1875, 1882) / Helen Mair Rawsthorne in Norois, n° 263 (avril - juin 2022)PermalinkEnriching the metadata of map images: a deep learning approach with GIS-based data augmentation / Yingjie Hu in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)PermalinkExploring the association between street built environment and street vitality using deep learning methods / Yunqin Li in Sustainable Cities and Society, vol 79 (April 2022)PermalinkIdentifying locations for new bike-sharing stations in Glasgow: an analysis of spatial equity and demand factors / Jeneva Beairsto in Annals of GIS, vol 28 n° 2 (April 2022)Permalink