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Integrating a forward feature selection algorithm, random forest, and cellular automata to extrapolate urban growth in the Tehran-Karaj region of Iran / Hossein Shafizadeh-Moghadam in Computers, Environment and Urban Systems, vol 87 (May 2021)
[article]
Titre : Integrating a forward feature selection algorithm, random forest, and cellular automata to extrapolate urban growth in the Tehran-Karaj region of Iran Type de document : Article/Communication Auteurs : Hossein Shafizadeh-Moghadam, Auteur ; Masoud Minaei, Auteur ; Robert Gilmore Pontius Jr, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 101595 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] automate cellulaire
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] croissance urbaine
[Termes IGN] extrapolation
[Termes IGN] image Landsat
[Termes IGN] modèle de simulation
[Termes IGN] occupation du sol
[Termes IGN] Téhéran
[Termes IGN] utilisation du solRésumé : (auteur) This paper couples a Forward Feature Selection algorithm with Random Forest (FFS-RF) to create a transition index map, which then guides the spatial allocation for the extrapolation of urban growth using a Cellular Automata model. We used Landsat imagery to generate land cover maps at the years 1998, 2008, and 2018 for the Tehran-Karaj Region (TKR) in Iran. The FFS-RF considered the independent variables of slope, altitude, and distances from urban, crop, greenery, barren, and roads. The FFS-RF revealed temporal non-stationary of drivers from 1998–2008 to 2008–2018. The FFS-RF detected that altitude and distance from greenery were the most important drivers of urban growth during 1998–2008, then distances from crop and barren were the most important drivers during 2008–2018. We used the Total Operating Characteristic to evaluate the transition index maps. Validation during 2008–2018 showed that FFS-RF produced a transition index map that had predictive power no better than an allocation of urban growth near existing urban. Simulation to 2060 extrapolated that Tehran, Karaj, and their adjacent cities will interconnect spatially to form a gigantic city-region. Numéro de notice : A2021-274 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101595 Date de publication en ligne : 16/02/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101595 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97357
in Computers, Environment and Urban Systems > vol 87 (May 2021) . - n° 101595[article]Numerical modelling for analysis of the effect of different urban green spaces on urban heat load patterns in the present and in the future / Tamás Gál in Computers, Environment and Urban Systems, vol 87 (May 2021)
[article]
Titre : Numerical modelling for analysis of the effect of different urban green spaces on urban heat load patterns in the present and in the future Type de document : Article/Communication Auteurs : Tamás Gál, Auteur ; Sándor István Mahó, Auteur ; Norà Skarbit, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 101600 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] arbre urbain
[Termes IGN] changement climatique
[Termes IGN] climat urbain
[Termes IGN] espace vert
[Termes IGN] flore urbaine
[Termes IGN] forêt périurbaine
[Termes IGN] forêt urbaine
[Termes IGN] Hongrie
[Termes IGN] ilot thermique urbain
[Termes IGN] image à haute résolution
[Termes IGN] modèle de simulation
[Termes IGN] modèle dynamique
[Termes IGN] parc urbain
[Termes IGN] planification urbaine
[Termes IGN] série temporelle
[Termes IGN] utilisation du solRésumé : (auteur) This paper focuses on urban green spaces in terms of climate and human thermal comfort containing their effect on heat load mitigation. It incorporates a modelling study in which the role of green spaces was investigated in terms of heat stress modification by applying MUKLIMO_3 model. During the experiment, the thermal effects of dense trees, scattered trees, grasslands and mixed green infrastructure has been investigated in the case of Szeged (Hungary) and assessed using different climate indices. The investigations encompassed 3 climatological time periods (1981–2010, 2021–2050 and 2071–2100) and two emission scenarios for future climate (RCP4.5 and RCP8.5). It was found that urban green spaces (e.g. parks) generally cool the environment, although, the cooling potential of the different green types differs. The highest reduction of heat load was found in the case of large urban parks comprising of dense trees near the downtown. The spatial extension of detected cooling was found small. However, it would increase during the future, especially in the case of grasslands. For urban planners, it is highly recommended to introduce new green sites within a city and to increase the spatial extension of the existing ones to mitigate and adapt to the impacts of climate change in the urban environment. Numéro de notice : A2021-276 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/URBANISME Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101600 Date de publication en ligne : 25/01/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101600 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97362
in Computers, Environment and Urban Systems > vol 87 (May 2021) . - n° 101600[article]A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media / Yi Bao in International journal of geographical information science IJGIS, vol 35 n° 4 (April 2021)
[article]
Titre : A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media Type de document : Article/Communication Auteurs : Yi Bao, Auteur ; Zhou Huang, Auteur ; Linna Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 639 - 660 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données spatiotemporelles
[Termes IGN] géopositionnement
[Termes IGN] graphe
[Termes IGN] modèle de simulation
[Termes IGN] point d'intérêt
[Termes IGN] réseau social
[Termes IGN] service fondé sur la position
[Termes IGN] utilisateur
[Termes IGN] Wuhan (Chine)Résumé : (auteur) Location prediction based on spatio-temporal footprints in social media is instrumental to various applications, such as travel behavior studies, crowd detection, traffic control, and location-based service recommendation. In this study, we propose a model that uses geotags of social media to predict the potential area containing users’ next locations. In the model, we utilize HiSpatialCluster algorithm to identify clustering areas (CAs) from check-in points. CA is the basic spatial unit for predicting the potential area containing users’ next locations. Then, we use the LINE (Large-scale Information Network Embedding) to obtain the representation vector of each CA. Finally, we apply BiLSTM-CNN (Bidirectional Long Short-Term Memory-Convolutional Neural Network) for location prediction. The results show that the proposed ensemble model outperforms the single LSTM or CNN model. In the case study that identifies 100 CAs out of Weibo check-ins collected in Wuhan, China, the Top-5 predicted areas containing next locations amount to an 80% accuracy. The high accuracy is of great value for recommendation and prediction on areal unit. Numéro de notice : A2021-268 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1808896 Date de publication en ligne : 26/08/2020 En ligne : https://doi.org/10.1080/13658816.2020.1808896 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97324
in International journal of geographical information science IJGIS > vol 35 n° 4 (April 2021) . - pp 639 - 660[article]Urban growth analysis and simulations using cellular automata and geo-informatics: comparison between Almaty and Astana in Kazakhstan / Aigerim Ilyassova in Geocarto international, vol 36 n° 5 ([15/03/2021])
[article]
Titre : Urban growth analysis and simulations using cellular automata and geo-informatics: comparison between Almaty and Astana in Kazakhstan Type de document : Article/Communication Auteurs : Aigerim Ilyassova, Auteur ; Lakshmi Kantakumar, Auteur ; Doreen S. Boyd, Auteur Année de publication : 2021 Article en page(s) : pp 520 - 539 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] automate cellulaire
[Termes IGN] croissance urbaine
[Termes IGN] dynamique spatiale
[Termes IGN] étalement urbain
[Termes IGN] image Landsat
[Termes IGN] Kazakhstan
[Termes IGN] modèle de simulation
[Termes IGN] modélisation spatiale
[Termes IGN] occupation du sol
[Termes IGN] planification urbaine
[Termes IGN] simulation spatiale
[Termes IGN] système d'information géographiqueRésumé : (auteur) In this research, the SLEUTH urban growth model is calibrated and validated for the first time to post Soviet Union cities. The aim of the study is to monitor, assess, simulate and compare the spatiotemporal urban growth dynamics and spatial patterns of the two largest cities Almaty and Astana using free remote sensing data. The urban expansion metrics and SLEUTH model are used to assess the urban growth dynamics. Though the capital has been moved to Astana from Almaty in 1998, Almaty is still developing faster than Astana. The urban growth simulation results from SLEUTH show Astana will surpass the urban growth of Almaty to emerge as the largest city in Kazakhstan by 2030. Astana may experience more leapfrog and ribbon developments. In Almaty, the urban growth may likely to take place in north and north-west parts. Numéro de notice : A2021-251 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1618923 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1618923 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97273
in Geocarto international > vol 36 n° 5 [15/03/2021] . - pp 520 - 539[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2021051 RAB Revue Centre de documentation En réserve L003 Disponible Analysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest / Seyedeh Kosar Hamidi in Annals of Forest Science, vol 78 n° 1 (March 2021)
[article]
Titre : Analysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest Type de document : Article/Communication Auteurs : Seyedeh Kosar Hamidi, Auteur ; Eric K. Zenner, Auteur ; Mahmoud Bayat, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Acer velutinum
[Termes IGN] Alnus cordata
[Termes IGN] analyse comparative
[Termes IGN] apprentissage automatique
[Termes IGN] Carpinus betulus
[Termes IGN] classification barycentrique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] dynamique de la végétation
[Termes IGN] écosystème forestier
[Termes IGN] Fagus orientalis
[Termes IGN] forêt inéquienne
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] Iran
[Termes IGN] modèle de croissance végétale
[Termes IGN] modèle de simulation
[Termes IGN] peuplement mélangé
[Termes IGN] régression linéaire
[Termes IGN] volume en bois
[Vedettes matières IGN] SylvicultureRésumé : (auteur) Key message: We modeled 10-year net stand volume growth with four machine learning (ML) methods, i.e., artificial neural networks (ANN), support vector machines (SVM), random forests (RF), and nearest neighbor analysis (NN), and with linear regression analysis. Incorporating interactions of multiple variables, the ML methods ANN and SVM predicted nonlinear system behavior and unraveled complex relations with greater accuracy than regression analysis.
Context: Investigating the quantitative and qualitative characteristics of short-term forest dynamics is essential for testing whether the desired goals in forest-ecosystem conservation and restoration are achieved. Inventory data from the Jojadeh section of the Farim Forest located in the uneven-aged, mixed Hyrcanian Forest were used to model and predict 10-year net annual stand volume increment with new machine learning technologies.
Aims: The main objective of this study was to predict net annual stand volume increment as the preeminent factor of forest growth and yield models.
Methods: In the current study, volume increment was modeled from two consecutive inventories in 2003 and 2013 using four machine learning techniques that used physiographic data of the forest as input for model development: (i) artificial neural networks (ANN), (ii) support vector machines (SVM), (iii) random forests (RF), and (iv) nearest neighbor analysis (NN). Results from the various machine learning technologies were compared against results produced with regression analysis.
Results: ANNs and SVMs with a linear kernel function that incorporated field-measurements of terrain slope and aspect as input variables were able to predict plot-level volume increment with a greater accuracy (94%) than regression analysis (87%).
Conclusion: These results provide compelling evidence for the added utility of machine learning technologies for modeling plot-level volume increment in the context of forest dynamics and management.Numéro de notice : A2021-071 Affiliation des auteurs : non IGN Thématique : FORET/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-01011-6 Date de publication en ligne : 12/01/2021 En ligne : https://doi.org/10.1007/s13595-020-01011-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96794
in Annals of Forest Science > vol 78 n° 1 (March 2021) . - n° 4[article]Machine learning in ground motion prediction / Farid Khosravikia in Computers & geosciences, vol 148 (March 2021)PermalinkModelling the effect of landmarks on pedestrian dynamics in urban environments / Gabriele Filomena in Computers, Environment and Urban Systems, vol 86 (March 2021)PermalinkThe Salem simulator version 2.0: a tool for predicting the productivity of pure and mixed stands and simulating management operations / Raphaël Aussenac in Open Research Europe, vol 2021 ([01/03/2021])PermalinkA 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)PermalinkOptimization of multi-ecosystem model ensembles to simulate vegetation growth at the global scale / Linling Tang in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)PermalinkA quantitative assessment of rockfall influence on forest structure in the Swiss Alps / Christine Moos in European Journal of Forest Research, vol 140 n° 1 (February 2021)PermalinkStand-scale climate change impacts on forests over large areas: transient responses and projection uncertainties / NIca Huber in Ecological Applications, vol 31 ([01/02/2021])PermalinkPermalinkApport de la télédétection pour la simulation spatialisée des composantes du bilan carbone des cultures et des effets d'atténuation biogéochimiques et biogéophysiques des cultures intermédiaires / Gaétan Pique (2021)PermalinkPermalinkPermalinkConvex hull: another perspective about model predictions and map derivatives from remote sensing data / Jean-Pierre Renaud (2021)PermalinkFlood mapping from radar remote sensing using automated image classification techniques / Lisa Landuyt (2021)PermalinkPermalinkHow validation through model exploration empowers theories of spatial complexity : example of urban systems / Juste Raimbault (2021)PermalinkInferencing hourly traffic volume using data-driven machine learning and graph theory / Zhiyan Yi in Computers, Environment and Urban Systems, vol 85 (January 2021)PermalinkIntegrating multilayer perceptron neural nets with hybrid ensemble classifiers for deforestation probability assessment in Eastern India / Sunil Saha in Geomatics, Natural Hazards and Risk, vol 12 n° 1 (2021)PermalinkModeling the risk of robbery in the city of Tshwane, South Africa / Nicolas Kemp in Cartography and Geographic Information Science, vol 48 n° 1 (January 2021)PermalinkModélisation et simulation de comportements piétons réalistes en espace partagé avec un véhicule autonome / manon Prédhumeau (2021)PermalinkModelling landslide hazards under global changes: the case of a Pyrenean valley / Séverine Bernardie in Natural Hazards and Earth System Sciences, vol 21 n° 1 (January 2021)PermalinkRegNet: a neural network model for predicting regional desirability with VGI data / Wenzhong Shi in International journal of geographical information science IJGIS, vol 35 n° 1 (January 2021)PermalinkSpatiotemporal patterns of urbanization during the last four decades in Switzerland and their impacts on urban heat islands / Marti Bosch Padros (2021)PermalinkTurgor – a limiting factor for radial growth in mature conifers along an elevational gradient / Richard L. 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