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Improving the reliability of landslide susceptibility mapping through spatial uncertainty analysis: a case study of Al Hoceima, Northern Morocco / Hassane Rahali in Geocarto international, vol 34 n° 1 ([01/01/2019])
[article]
Titre : Improving the reliability of landslide susceptibility mapping through spatial uncertainty analysis: a case study of Al Hoceima, Northern Morocco Type de document : Article/Communication Auteurs : Hassane Rahali, Auteur Année de publication : 2019 Article en page(s) : pp 43 - 77 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse comparative
[Termes IGN] analyse des risques
[Termes IGN] effondrement de terrain
[Termes IGN] géomorphologie locale
[Termes IGN] incertitude géométrique
[Termes IGN] lithologie
[Termes IGN] Maroc
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] méthode fiable
[Termes IGN] modèle de simulation
[Termes IGN] processus stochastique
[Termes IGN] régression logistique
[Termes IGN] théorème de Bayes
[Termes IGN] zone à risqueRésumé : (auteur) This paper aims at providing an answer as to whether generalization obtained with data-driven modelling can be used to gauge the plausibility of the physically based (PB) model’s prediction. Two statistical models namely; Weight of Evidence (WofE) and Logistic Regression (LR), and a PB model using the infinite slope assumptions were evaluated and compared with respect to their abilities to predict susceptible areas to shallow landslides at the 1:10.000 urban scale. Threshold-dependent performance metrics showed that the three methods produced statistically comparable results in terms of success and prediction rates. However, with the Area Under the receiver operator Curve (AUC), statistical models are more accurate (88.7 and 84.6% for LR and WofE, respectively) than the PB model (only 69.8%). Nevertheless, in such data-sparse situation, the usual approaches for validation, i.e. comparing observed with predicted data, are insufficient, formal uncertainty analysis (UA) is a means for evaluating the validity and reliability of the model. We then refitted the PB model using a stochastic modification of the infinite slope stability model input scheme using Monte Carlo (MC) method backed with sensitivity analysis (SA). For statistical models, we used an informal Student t-test for estimating the certainty of the predicted probability (PP) at each location. Both modelling outputs independently show a high validity; and whereas the level of confidence in LR and WofE models remained the same after performance re-evaluation, the accuracy of the PB model showed an improvement (AUC = 72%). This result is reasonable and provides a further validation of PB model. So, in urban slope analysis, where PB diagnostic is necessary, statistical and PB modelling may play equally supportive roles in landslide hazard assessment. Numéro de notice : A2019-219 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1357767 Date de publication en ligne : 10/08/2017 En ligne : https://doi.org/10.1080/10106049.2017.1357767 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92737
in Geocarto international > vol 34 n° 1 [01/01/2019] . - pp 43 - 77[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2019011 RAB Revue Centre de documentation En réserve L003 Disponible CAVIAR: an R package for checking, displaying and processing wood-formation-monitoring data / Cyrille B.K. Rathgeber in Tree Physiology, vol 38 n° 8 (August 2018)
[article]
Titre : CAVIAR: an R package for checking, displaying and processing wood-formation-monitoring data Type de document : Article/Communication Auteurs : Cyrille B.K. Rathgeber, Auteur ; Philippe Santenoise, Auteur ; Henri E. Cuny , Auteur Année de publication : 2018 Projets : ARBRE / AgroParisTech (2007 -) Article en page(s) : pp 1246 - 1260 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] cerne
[Termes IGN] données allométriques
[Termes IGN] dynamique de la végétation
[Termes IGN] forêt boréale
[Termes IGN] forêt tempérée
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] Loi de Gompertz
[Termes IGN] phénologie
[Termes IGN] Pinophyta
[Termes IGN] R (langage)
[Termes IGN] régression logistique
[Termes IGN] visualisation de données
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) In the last decade, the pervasive question of climate change impacts on forests has revived investigations on intra-annual dynamics of wood formation, involving disciplines such as plant ecology, tree physiology and dendrochronology. This resulted in the creation of many research groups working on this topic worldwide and a rapid increase in the number of studies and publications. Wood-formation-monitoring studies are generally based on a common conceptual model describing xylem cell formation as the succession of four differentiation phases (cell division, cell enlargement, cell wall thickening and mature cells). They generally use the same sampling techniques, sample preparation methods and anatomical criteria to separate between differentiation zones and discriminate and count forming xylem cells, resulting in very similar raw data. However, the way these raw data are then processed, producing the elaborated data on which statistical analyses are performed, still remains quite specific to each individual study. Thereby, despite very similar raw data, wood-formation-monitoring studies yield results that are still quite difficult to compare. CAVIAR—an R package specifically dedicated to the verification, visualization and manipulation of wood-formation-monitoring data—can help to improve this situation. Initially, CAVIAR was built to provide efficient algorithms to compute critical dates of wood formation phenology for conifers growing in temperate and cold environments. Recently, we developed it further to check, display and process wood-formation-monitoring data. Thanks to new and upgraded functions, raw data can now be consistently verified, standardized and modelled (using logistic regressions and Gompertz functions), in order to describe wood phenology and intra-annual dynamics of tree-ring formation. We believe that CAVIAR will help strengthening the science of wood formation dynamics by effectively contributing to the standardization of its concepts and methods, making thereby possible the comparison between data and results from different studies. Numéro de notice : A2018-657 Affiliation des auteurs : IGN+Ext (2012-2019) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/treephys/tpy054 Date de publication en ligne : 19/05/2018 En ligne : https://doi.org/10.1093/treephys/tpy054 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93813
in Tree Physiology > vol 38 n° 8 (August 2018) . - pp 1246 - 1260[article]
Titre : Introduction to Deep Learning : From Logical Calculus to Artificial Intelligence Type de document : Monographie Auteurs : Sandro Skansi, Auteur Editeur : Springer Nature Année de publication : 2018 Importance : 196 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-319-73004-2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage profond
[Termes IGN] classification
[Termes IGN] codage
[Termes IGN] estimation par noyau
[Termes IGN] matrice de covariance
[Termes IGN] Perceptron multicouche
[Termes IGN] Python (langage de programmation)
[Termes IGN] régression logistique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] réseau neuronal convolutif
[Termes IGN] sciences cognitives
[Termes IGN] théorie des probabilitésRésumé : (auteur) This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website.
Topics and features:
Introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning
Discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network
Examines convolutional neural networks, and the recurrent connections to a feed-forward neural network
Describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning
Presents a brief history of artificial intelligence and neural networks, and reviews interesting
open research problems in deep learning and connectionism
This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.Note de contenu : 1- From Logic to Cognitive Science
2- Mathematical and Computational Prerequisites
3- Machine Learning Basics
4- Feedforward Neural Networks
5- Modifications and Extensions to a Feed-Forward Neural Network
6- Convolutional Neural Networks
7- Recurrent Neural Networks
8- Autoencoders
9- Neural Language Models
10- An Overview of Different Neural Network Architectures
11- ConclusionNuméro de notice : 25787 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Monographie En ligne : https://doi.org/10.1007/978-3-319-73004-2 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94990 Modeling dynamic urban land-use change with geographical cellular automata and generalized pattern search-optimized rules / Yongjiu Feng in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)
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Titre : Modeling dynamic urban land-use change with geographical cellular automata and generalized pattern search-optimized rules Type de document : Article/Communication Auteurs : Yongjiu Feng, Auteur Année de publication : 2017 Article en page(s) : pp 1198 - 1219 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] algorithme génétique
[Termes IGN] analyse diachronique
[Termes IGN] automate cellulaire
[Termes IGN] base de règles
[Termes IGN] changement d'utilisation du sol
[Termes IGN] données GPS
[Termes IGN] jointure spatiale
[Termes IGN] Kiangsou (Chine)
[Termes IGN] modèle de simulation
[Termes IGN] prédiction
[Termes IGN] régression logistique
[Termes IGN] simulation
[Termes IGN] zone urbaineRésumé : (auteur) A novel generalized pattern search (GPS)-based cellular automata (GPS-CA) model was developed to simulate urban land-use change in a GIS environment. The model is built on a fitness function that computes the difference between the observed results produced from remote-sensing images and the simulated results produced by a general CA model. GPS optimization incorporating genetic algorithms (GAs) searches for the minimum difference, i.e. the smallest accumulated residuals, in fitting the CA transition rules. The CA coefficients captured by the GPS method have clear physical meanings that are closely associated with the dynamic mechanisms of land-use change. The GPS-CA model was applied to simulate urban land-use change in Kunshan City in the Yangtze River Delta from 2000 to 2015. The results show that the GPS method had a smaller root mean squared error (0.2821) than a logistic regression (LR) method (0.5256) in fitting the CA transition rules. The GPS-CA model thus outperformed the LR-CA model, with an overall accuracy improvement of 4.7%. As a result, the GPS-CA model should be a superior tool for modeling land-use change as well as predicting future scenarios in response to different conditions to support the sustainable urban development. Numéro de notice : A2017-244 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1287368 En ligne : http://dx.doi.org/10.1080/13658816.2017.1287368 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85180
in International journal of geographical information science IJGIS > vol 31 n° 5-6 (May-June 2017) . - pp 1198 - 1219[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2017031 RAB Revue Centre de documentation En réserve L003 Disponible 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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