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Comparison of spatial modelling approaches to simulate urban growth: a case study on Udaipur city, India / Biswajit Mondal in Geocarto international, vol 35 n° 4 ([15/03/2020])  

Public [article]  inGeocarto international > vol 35 n° 4 [15/03/2020] . - pp 411 - 433 Titre : Comparison of spatial modelling approaches to simulate urban growth: a case study on Udaipur city, India Type de document : Article/Communication Auteurs : Biswajit Mondal, Auteur ; Suman Chakraborti, Auteur ; Dipendra Nath Das, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 411 - 433 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] analyse multicritère [Termes descripteurs IGN] automate cellulaire [Termes descripteurs IGN] chaîne de Markov [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] étalement urbain [Termes descripteurs IGN] Inde [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] modélisation spatiale [Termes descripteurs IGN] pente [Termes descripteurs IGN] Perceptron multicouche [Termes descripteurs IGN] utilisation du sol Résumé : (auteur) Assessment of past and future urban growth processes helps the decision makers to evaluate and formulate the policy documents. In an attempt to make such assessments, this study compares three commonly used urban growth models: Multicriteria Cellular Automata-Markov Chain (MCCA-MC), Multi-Layer Perception Markov Chain (MLP-MC), and the Slope, Land use, Exclusion, Urban Extent, Transportation and Hillshade (SLEUTH). This study has taken into account the land use and land cover data for the years, 1977, 1992, 2000, 2008, 2016 and prepared driving variables for urban growth. The KAPPA index of agreement indicates that the MCCA-MC, MLP-MC and SLEUTH models avoid errors by 94%, 93%, and 92% respectively. Models forecast that about 156.96 km2, 157.43 km2 and 142.43 km2 built-up areas will emerge through the process of urbanization by 2031 in the city of Udaipur. However, this assessment identified that all the models are embodied with their own advantages and disadvantages while serving specific purposes. While the MCCA-MC and MLP-MC provides a good account of the urban spread, the SLEUTH identifies the new isolated growth centres more accurately. Numéro de notice : A2020-100 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1520922 date de publication en ligne : 03/01/2019 En ligne : https://doi.org/10.1080/10106049.2018.1520922 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94691 [article]

Bayesian inversion of convolved hidden Markov models with applications in reservoir prediction / Torstein Fjeldstad in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 3 (March 2020) . - pp 1957 - 1968 Titre : Bayesian inversion of convolved hidden Markov models with applications in reservoir prediction Type de document : Article/Communication Auteurs : Torstein Fjeldstad, Auteur ; Henning Omre, Auteur Année de publication : 2020 Article en page(s) : pp 1957 - 1968 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] amplitude [Termes descripteurs IGN] analyse mathématique [Termes descripteurs IGN] approximation [Termes descripteurs IGN] chaîne de Markov [Termes descripteurs IGN] filtrage numérique d'image [Termes descripteurs IGN] lithologie [Termes descripteurs IGN] méthode de Monte-Carlo par chaînes de Markov [Termes descripteurs IGN] méthode du maximum de vraisemblance (estimation) [Termes descripteurs IGN] modèle d'inversion [Termes descripteurs IGN] modèle mathématique [Termes descripteurs IGN] processus gaussien [Termes descripteurs IGN] sismicité Résumé : (Auteur) The efficient assessment of convolved hidden Markov models is discussed. The bottom layer is defined as an unobservable categorical first-order Markov chain, whereas the middle layer is assumed to be a Gaussian spatial variable conditional on the bottom layer. Hence, this layer appears marginally as a Gaussian mixture spatial variable. We observe the top layer as a convolution of the middle layer with Gaussian errors. The focus is on assessing the categorical and Gaussian mixture variables given the observations, and we operate in a Bayesian inversion framework. The model is defined to perform the inversion of subsurface seismic amplitude-versus-offset data into lithology/fluid classes and to assess the associated seismic material properties. Due to the spatial coupling in the likelihood functions, evaluation of the posterior normalizing constant is computationally demanding, and brute-force, single-site updating Markov chain Monte Carlo (MCMC) algorithms converge far too slowly to be useful. We construct two classes of approximate posterior models, which we assess analytically and efficiently using the recursive forward–backward algorithm. These approximate posterior densities are used as proposal densities in an independent proposal MCMC algorithm to determine the correct posterior model. A set of synthetic realistic examples is presented. The proposed approximations provide efficient proposal densities, which results in acceptance probabilities in the range 0.10–0.50 in the MCMC algorithm. A case study of lithology/fluid seismic inversion is presented. The lithology/fluid classes and the seismic material properties can be reliably predicted. Numéro de notice : A2020-093 Thématique : IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2951205 date de publication en ligne : 26/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2951205 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94667 [article]

Land use and land cover change modeling and future potential landscape risk assessment using Markov-CA model and analytical hierarchy process / Biswajit Nath in ISPRS International journal of geo-information, vol 9 n° 2 (February 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 2 (February 2020) Titre : Land use and land cover change modeling and future potential landscape risk assessment using Markov-CA model and analytical hierarchy process Type de document : Article/Communication Auteurs : Biswajit Nath, Auteur ; Zhihua Wang, Auteur ; Yong Ge, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] aménagement paysager [Termes descripteurs IGN] automate cellulaire [Termes descripteurs IGN] chaîne de Markov [Termes descripteurs IGN] changement d'occupation du sol [Termes descripteurs IGN] Chine [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] faille géologique [Termes descripteurs IGN] modèle de Markov [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] processus d'analyse hiérarchisée [Termes descripteurs IGN] risque environnemental [Termes descripteurs IGN] risque naturel [Termes descripteurs IGN] séisme [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] utilisation du sol Résumé : (auteur) Land use and land cover change (LULCC) has directly played an important role in the observed climate change. In this paper, we considered Dujiangyan City and its environs (DCEN) to study the future scenario in the years 2025, 2030, and 2040 based on the 2018 simulation results from 2007 and 2018 LULC maps. This study evaluates the spatial and temporal variations of future LULCC, including the future potential landscape risk (FPLR) area of the 2008 great (8.0 Mw) earthquake of south-west China. The Cellular automata–Markov chain (CA-Markov) model and multicriteria based analytical hierarchy process (MC-AHP) approach have been considered using the integration of remote sensing and GIS techniques. The analysis shows future LULC scenario in the years 2025, 2030, and 2040 along with the FPLR pattern. Based on the results of the future LULCC and FPLR scenarios, we have provided suggestions for the development in the close proximity of the fault lines for the future strong magnitude earthquakes. Our results suggest a better and safe planning approach in the Belt and Road Corridor (BRC) of China to control future Silk-Road Disaster, which will also be useful to urban planners for urban development in a safe and sustainable manner. Numéro de notice : A2020-112 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9020134 date de publication en ligne : 24/02/2020 En ligne : https://doi.org/10.3390/ijgi9020134 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94717 [article]

Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata / Tingting Xu in International journal of geographical information science IJGIS, vol 33 n° 10 (October 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 33 n° 10 (October 2019) . - pp 1960 - 1983 Titre : Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata Type de document : Article/Communication Auteurs : Tingting Xu, Auteur ; Jay Gao, Auteur ; Giovanni Coco, Auteur Année de publication : 2019 Article en page(s) : pp 1960 - 1983 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] Auckland [Termes descripteurs IGN] automate cellulaire [Termes descripteurs IGN] base de données d'occupation du sol [Termes descripteurs IGN] chaîne de Markov [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] étalement urbain [Termes descripteurs IGN] Kappa de Cohen [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] morphologie urbaine [Termes descripteurs IGN] optimisation (mathématiques) Résumé : (auteur) Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone. Numéro de notice : A2019-428 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1600701 date de publication en ligne : 05/04/2019 En ligne : https://doi.org/10.1080/13658816.2019.1600701 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93561 [article]

Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change / Hao Wu in International journal of geographical information science IJGIS, Vol 33 n° 5-6 (May - June 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > Vol 33 n° 5-6 (May - June 2019) . - pp 1040 - 1061 Titre : Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change Type de document : Article/Communication Auteurs : Hao Wu, Auteur ; Zhen Li, Auteur ; Keith C. Clarke, Auteur ; Wenzhong Shi, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1040 - 1061 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse de sensibilité [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] automate cellulaire [Termes descripteurs IGN] chaîne de Markov [Termes descripteurs IGN] changement d'occupation du sol [Termes descripteurs IGN] changement d'utilisation du sol [Termes descripteurs IGN] image Landsat-TM [Termes descripteurs IGN] modèle de Markov [Termes descripteurs IGN] simulation Résumé : (Auteur) Understanding the spatial scale sensitivity of cellular automata is crucial for improving the accuracy of land use change simulation. We propose a framework based on a response surface method to comprehensively explore spatial scale sensitivity of the cellular automata Markov chain (CA-Markov) model, and present a hybrid evaluation model for expressing simulation accuracy that merges the strengths of the Kappa coefficient and of Contagion index. Three Landsat-Thematic Mapper remote sensing images of Wuhan in 1987, 1996, and 2005 were used to extract land use information. The results demonstrate that the spatial scale sensitivity of the CA-Markov model resulting from individual components and their combinations are both worthy of attention. The utility of our proposed hybrid evaluation model and response surface method to investigate the sensitivity has proven to be more accurate than the single Kappa coefficient method and more efficient than traditional methods. The findings also show that the CA-Markov model is more sensitive to neighborhood size than to cell size or neighborhood type considering individual component effects. Particularly, the bilateral and trilateral interactions between neighborhood and cell size result in a more remarkable scale effect than that of a single cell size. Numéro de notice : A2019-443 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1568441 date de publication en ligne : 18/02/2019 En ligne : https://doi.org/10.1080/13658816.2019.1568441 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92777 [article]

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