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Landslide susceptibility mapping using Naïve Bayes and Bayesian network models in Umyeonsan, Korea / Sunmin Lee in Geocarto international, vol 35 n° 15 ([01/11/2020])  

Public [article]  inGeocarto international > vol 35 n° 15 [01/11/2020] . - pp 1665 - 1679 Titre : Landslide susceptibility mapping using Naïve Bayes and Bayesian network models in Umyeonsan, Korea Type de document : Article/Communication Auteurs : Sunmin Lee, Auteur ; Moung-Jin Lee, Auteur ; Hyung-Sup Jung, Auteur ; Saro Lee, Auteur Année de publication : 2020 Article en page(s) : pp 1665 - 1679 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] carte de la végétation [Termes IGN] carte forestière [Termes IGN] carte topographique [Termes IGN] cartographie des risques [Termes IGN] catastrophe naturelle [Termes IGN] Corée du sud [Termes IGN] effondrement de terrain [Termes IGN] modèle stochastique [Termes IGN] réseau bayesien [Termes IGN] système d'information géographique [Termes IGN] zone urbaine Résumé : (auteur) In recent years, machine learning techniques have been increasingly applied to the assessment of various natural disasters, including landslides and floods. Machine learning techniques can be used to make predictions based on the relationships among events and their influencing factors. In this study, a machine learning approaches were applied based on landslide location data in a geographic information system environment. Topographic maps were used to determine the topographical factors. Additional soil and forest parameters were examined using information obtained from soil and forest maps. A total of 17 factors affecting landslide occurrence were selected and a spatial database was constructed. Naïve Bayes and Bayesian network models were applied to predict landslides based on selected risk factors. The two models showed accuracies of 78.3 and 79.8%, respectively. The results of this study provide a useful foundation for effective strategies to prevent and manage landslides in urban areas. Numéro de notice : A2020-658 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1585482 date de publication en ligne : 16/04/2019 En ligne : https://doi.org/10.1080/10106049.2019.1585482 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96130 [article]

Volcano-seismic transfer learning and uncertainty quantification with bayesian neural networks / Angel Bueno in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 2 (February 2020) . - pp Titre : Volcano-seismic transfer learning and uncertainty quantification with bayesian neural networks Type de document : Article/Communication Auteurs : Angel Bueno, Auteur ; Carmen Benitez, Auteur ; Silvio De Angelis, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] apprentissage profond [Termes IGN] classification bayesienne [Termes IGN] classification par réseau neuronal [Termes IGN] forme d'onde [Termes IGN] incertitude des données [Termes IGN] réseau bayesien [Termes IGN] réseau neuronal profond [Termes IGN] Russie [Termes IGN] séisme [Termes IGN] sismologie [Termes IGN] surveillance géologique [Termes IGN] volcanologie [Termes IGN] Washington (Etats-Unis ; état) Résumé : (auteur) Over the past few years, deep learning (DL) has emerged as an important tool in the fields of volcano and earthquake seismology. However, these methods have been applied without performing thorough analyses of the associated uncertainties. Here, we propose a solution to enhance volcano-seismic monitoring systems, through probabilistic Bayesian DL; we implement and demonstrate a workflow for waveform classification, rapid quantification of the associated uncertainty, and link these uncertainties to changes in volcanic unrest. Specifically, we introduce Bayesian neural networks (BNNs) to perform event identification, classification, and their estimated uncertainty on data gathered at two active volcanoes, Mount St. Helens, Washington, USA, and Bezymianny, Kamchatka, Russia. We demonstrate how BNNs achieve excellent performance (92.08%) in discriminating both the type of event and its origin when the two data sets are merged together, and no additional training information is provided. Finally, we demonstrate that the data representations learned by the BNNs are transferable across different eruptive periods. We also find that the estimated uncertainty is related to changes in the state of unrest at the volcanoes and propose that it could be used to gauge whether the learned models may be exported to other eruptive scenarios. Numéro de notice : A2020-094 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2941494 date de publication en ligne : 07/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2941494 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94657 [article]

Bayesian inference / Javier Prieto Tejedor (2017)  

Public Titre : Bayesian inference Type de document : Monographie Auteurs : Javier Prieto Tejedor, Editeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2017 Importance : 378 p. Format : 19 x 27 cm ISBN/ISSN/EAN : 978-953-51-3578-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] estimation bayesienne [Termes IGN] filtrage bayésien [Termes IGN] inférence statistique [Termes IGN] méthode de Monte-Carlo par chaînes de Markov [Termes IGN] régression [Termes IGN] réseau bayesien Résumé : (éditeur) The range of Bayesian inference algorithms and their different applications has been greatly expanded since the first implementation of a Kalman filter by Stanley F. Schmidt for the Apollo program. Extended Kalman filters or particle filters are just some examples of these algorithms that have been extensively applied to logistics, medical services, search and rescue operations, or automotive safety, among others. This book takes a look at both theoretical foundations of Bayesian inference and practical implementations in different fields. It is intended as an introductory guide for the application of Bayesian inference in the fields of life sciences, engineering, and economics, as well as a source document of fundamentals for intermediate Bayesian readers. Note de contenu : 1- Bayesian inference application 2- Node-level conflict measures in Bayesian hierarchical models based on directed acyclic graphs 3- Classifying by Bayesian method and some applications 4- Hypothesis testing for high-dimensional problems 5- Bayesian vs frequentist power functions to determine the optimal sample size: testing one sample binomial proportion using exact methods 6- Converting graphic relationships into conditional probabilities in Bayesian network 7- Bayesian estimation of multivariate autoregressive hidden Markov model with application to breast cancer biomarker modeling 8- Bayesian model averaging and compromising in dose-response studies 9- Two examples of Bayesian evidence synthesis with the hierarchical meta-regression approach 10- Bayesian modeling in genetics and genomicsvvv 11- Bayesian two-stage robust causal modeling with instrumental variables using student's t distributions 12- Bayesian hypothesis testing: an alternative to null hypothesis significance testing (NHST) in psychology and social sciences 13- Bayesian inference and compressed sensing 14- Sparsity in Bayesian signal estimation 15- Dynamic Bayesian network for time-dependent classification problems in robotics 16- A Bayesian model for investment decisions in early ventures 17- Recent advances in nonlinear filtering with a financial application to derivatives hedging under incomplete information 18- Airlines content recommendations based on passengers' choice using Bayesian belief networks Numéro de notice : 25957 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/66264 En ligne : https://doi.org/10.5772/66264 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96464

A Bayesian network-based method to alleviate the ill-posed inverse problem: A case study on leaf area index and canopy water content retrieval / Xingwen Quan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 12 (December 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 12 (December 2015) . - pp 6507 - 6517 Titre : A Bayesian network-based method to alleviate the ill-posed inverse problem: A case study on leaf area index and canopy water content retrieval Type de document : Article/Communication Auteurs : Xingwen Quan, Auteur ; Binbin He, Auteur ; Xing Li, Auteur Année de publication : 2015 Article en page(s) : pp 6507 - 6517 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] corrélation d'images [Termes IGN] image Landsat-8 [Termes IGN] Leaf Area Index [Termes IGN] probabilité [Termes IGN] problème inverse [Termes IGN] réseau bayesien [Termes IGN] teneur en eau de la végétation Résumé : (auteur) Retrieval of vegetation parameters from remotely sensed data using a radiative transfer model is generally hampered by the ill-posed inverse problem, which dramatically decreases the precision level of retrieved parameters. The purpose of this study was to use a Bayesian network-based method to allow the alleviation of the ill-posed inverse problem. This was achieved by introducing the correlations between the model free parameters into their prior joint probability distribution (PJPD), allowing the reduction of the probabilities of unrealistic combinations. Three sampling strategies intended to design three types of PJPDs that considered different correlations (represented by a correlation matrix) were presented. They were multivariate uniform distribution composed by independent free parameters, multivariate uniform distribution based on a simple correlation matrix, and multivariate Gaussian distribution based on a complicated correlation matrix, respectively. A case study of the presented method to retrieve leaf area index (LAI) and canopy water content (CWC) using the PROSAIL_5B (PROSPECT-5 + 4SAIL) model from Landsat 8 products was implemented. Results indicate that the presented method greatly improves the precision level of target parameters, with the coefficient of determination R2 of 0.69, 0.77, and 0.82 and root-mean-square error (RMSE) of 0.55, 0.51, and 0.44 m2 · m-2 for LAI and R2 = 0.68, 0.78, and 0.84 and RMSE = 230, 198, and 166 g · m-2 for CWC, respectively. Hence, the ill-posed inverse problem can be alleviated by the presented method, which can be widely applied for vegetation parameters retrieval. Numéro de notice : A2015-838 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2442999 date de publication en ligne : 30/06/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2442999 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79172 [article]

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Bayesian belief networks as a versatile method for assessing uncertainty in land-change modeling / Carsten Krüger in International journal of geographical information science IJGIS, vol 29 n° 1 (January 2015)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 29 n° 1 (January 2015) . - pp 111 - 131 Titre : Bayesian belief networks as a versatile method for assessing uncertainty in land-change modeling Type de document : Article/Communication Auteurs : Carsten Krüger, Auteur ; Tobia Lakes, Auteur Année de publication : 2015 Article en page(s) : pp 111 - 131 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] aide à la décision [Termes IGN] Amazonie [Termes IGN] Brésil [Termes IGN] changement d'occupation du sol [Termes IGN] changement d'utilisation du sol [Termes IGN] déboisement [Termes IGN] incertitude des données [Termes IGN] modèle conceptuel de données spatio-temporelles [Termes IGN] réseau bayesien [Termes IGN] théorie de Dempster-Shafer Résumé : (auteur) Land-use and land-cover change modeling helps us to understand the driving factors and impacts of human-induced land changes better, and depict likely future development paths. Uncertainty associated with various steps in the modeling process substantially influences the reliability of the results, but until now it has only rarely been addressed. In this study, we explore uncertainty in land-change modeling using a probabilistic approach based on Bayesian belief networks. We apply this approach to a case study of deforestation in the Brazilian Amazon and identify three modeling steps as sources of uncertainty: model structure, variable selection, and data preprocessing. For these three steps, we quantify the uncertainty and the respective impact on the outcome accuracy. The results indicate remarkable uncertainties in each of the steps. We demonstrate that a higher uncertainty in the land-change modeling process does not necessarily lead to a lower accuracy of the modeling outcome. Moreover, we show that the different uncertainty sources only slightly influence the ratio between quantity disagreement and allocation disagreement for the modeling outcome. We conclude that uncertainty is inherent in land-change modeling, and that future studies should address this uncertainty more explicitly to improve the robustness of modeling outcomes for science and decision-making. Numéro de notice : A2015-532 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2014.949265 En ligne : http://www.tandfonline.com/doi/full/10.1080/13658816.2014.949265 Format de la ressource électronique : URL bulletin Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77763 [article]

The Bayesian detection of discontinuities in a polynomial regression and its application to the cycle-slip problem / M.C. DE Lacy in Journal of geodesy, vol 82 n° 9 (September 2008)

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Semantic-sensitive satellite image retrieval / Y. Li in IEEE Transactions on geoscience and remote sensing, vol 45 n° 4 (April 2007)

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Optimisation en traitement du signal et de l'image / P. Siarry (2007)

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Application des réseaux bayésiens de classification dans les systèmes d'informatin géographique / Marie-Aline Cavarroc (2005)

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Hierarchical Bayesian nets for building extraction using dense digital surface models / A. Brunn in ISPRS Journal of photogrammetry and remote sensing, vol 53 n° 5 (September - October 1998)

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Révision d'information dans un SIG / Marie-Aline Cavarroc (1998)

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Special interim technical report / Robert M. Haralick (1969)

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