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Titre : Flood forecasting using machine learning methods Type de document : Monographie Auteurs : Fi-John Chang, Éditeur scientifique ; Kuolin Hsu, Éditeur scientifique ; Li-Chiu Chang, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 376 p. Format : 17 x 25 cm ISBN/ISSN/EAN : 978-3-03897-548-9 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image à haute résolution
[Termes IGN] inondation
[Termes IGN] modèle de simulation
[Termes IGN] modèle hydrographique
[Termes IGN] optimisation (mathématiques)
[Termes IGN] prévention des risques
[Termes IGN] réseau neuronal artificiel
[Termes IGN] ruissellementRésumé : (éditeur) Nowadays, the degree and scale of flood hazards has been massively increasing as a result of the changing climate, and large-scale floods jeopardize lives and properties, causing great economic losses, in the inundation-prone areas of the world. Early flood warning systems are promising countermeasures against flood hazards and losses. A collaborative assessment according to multiple disciplines, comprising hydrology, remote sensing, and meteorology, of the magnitude and impacts of flood hazards on inundation areas significantly contributes to model the integrity and precision of flood forecasting. Methodologically oriented countermeasures against flood hazards may involve the forecasting of reservoir inflows, river flows, tropical cyclone tracks, and flooding at different lead times and/or scales. Analyses of impacts, risks, uncertainty, resilience, and scenarios coupled with policy-oriented suggestions will give information for flood hazard mitigation. Emerging advances in computing technologies coupled with big-data mining have boosted data-driven applications, among which Machine Learning technology, with its flexibility and scalability in pattern extraction, has modernized not only scientific thinking but also predictive applications. This book explores recent Machine Learning advances on flood forecast and management in a timely manner and presents interdisciplinary approaches to modelling the complexity of flood hazards-related issues, with contributions to integrative solutions from a local, regional or global perspective. Note de contenu : Preface
1- Building an intelligent hydroinformatics integration platform for regional flood inundation warning systems
2- Flood prediction using machine learning models: Literature review
3- Forward prediction of runoff data in data-scarce basins with an improved ensemble empirical mode decomposition (EEMD) model
4- Extraction of urban water bodies from high-resolution remote-sensing imagery using
deep learning
5- Data pre-analysis and ensemble of various artificial neural networks for monthly
streamflow forecasting
6- Physical hybrid neural network model to forecast typhoon floods
7- Improving the Muskingum flood routing method using a hybrid of particle swarm
optimization and bat algorithm
8- Flood hydrograph prediction using machine learning methods
9- Flood routing in river reaches using a three-parameter Muskingum model coupled with an improved bat algorithm
10- New hybrids of ANFIS with several optimization algorithms for flood susceptibility modeling
11- Building ANN-based regional multi-step-ahead flood inundation forecast models
12- Identifying the sensitivity of ensemble streamflow prediction by artificial intelligence
13- Flood forecasting based on an improved extreme learning machine model combined with the backtracking search optimization algorithm
14- Dongting Lake water level forecast and its relationship with the three gorges dam based on a long short-term memory network
15- Multi-objective parameter estimation of improved Muskingum model by wolf pack algorithm and its application in Upper Hanjiang River, China
16- Flash-flood forecasting in an Andean mountain catchment—development of a step-wise
methodology based on the random forest algorithm
17- Deep learning with a long short-term memory networks approach for rainfall-runoff
simulation
18- Flood routing model with particle filter-based data assimilation for flash flood forecasting in the micro-model of lower Yellow River, China
19- Application of artificial neural networks for accuracy enhancements of real-time flood forecasting in the Imjin BasinNuméro de notice : 25927 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Monographie En ligne : https://doi.org/10.3390/books978-3-03897-549-6 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96181 Fostering the use of methods for geosimulation models sensitivity analysis and validation / Romain Reuillon (2019)
Titre : Fostering the use of methods for geosimulation models sensitivity analysis and validation Type de document : Article/Communication Auteurs : Romain Reuillon, Auteur ; Mathieu Leclaire, Auteur ; Juste Raimbault, Auteur ; Hélène Arduin, Auteur ; Paul Chapron , Auteur ; Guillaume Chérel, Auteur ; Etienne Delay, Auteur ; Pierre-François Lavallée, Auteur ; Jonathan Passerat-Palmbach, Auteur ; Pierre Peigne, Auteur ; Julien Perret , Auteur ; Sébastien Rey-Coyrehourcq, Auteur Editeur : Paris : Institut des systèmes complexes Année de publication : 2019 Conférence : ECTQG 2019, 21st European Colloquium on Theoretical and Quantitative Geography 05/09/2019 09/09/2019 Mondorf-Les-Bains Luxembourg Open Access Abstracts Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de sensibilité
[Termes IGN] données localisées
[Termes IGN] estimation bayesienne
[Termes IGN] modèle de simulation
[Termes IGN] ontologie
[Termes IGN] plateforme logicielleMots-clés libres : OpenMOLE Résumé : (auteur) In recent years, there has been a significant increase in the development of methods to explore, validate, calibrate and optimize geosimulation models. These methods and tools remain, however, underused by simulation communities, despite an ever improved and easier access to high performance computation facilities. The OpenMOLE model exploration software (Reuillon et al., 2013) is one of the reliable approaches fully dedicated to promote these techniques. This presentation offers some feedback on the recent initiative of a researcher school in model validation, focused around models and practices linked to the OpenMOLE platform. We present the iterative exploration and validation protocol developed during the school, with methods of increasing refinement deployed on a toy geosimulation model (spatialized prey-predator agent-based model of a zombie infection, with multi-modeling paradigms to include diverse processes for agent behavior). First, we illustrate classical sensitivity analysis methods (stochasticity, design of experiments, global sensitivity indices), and then specific methods to study spatial configuration sensitivity, evolutionary computation methods for calibration and diversity search, and Bayesian calibration methods. They are applied on diverse specific submodels, highlighting specific mechanisms of the model, in order to answer associated thematic questions. We also illustrate the comparison with competing model ontologies by calibrating an ODE-based model on data generated by the simulation model. We finally synthesize lessons learned in the final challenge part of the school, consisting of the autonomous exploration of a new model instance by participants, including defining a thematic question and applying appropriate validation methods. This experiment both introduces a broad overview of new geosimulation model methods, and suggests ways to disseminate these into the modeling communities through similar pedagogical implementations. Numéro de notice : C2019-048 Affiliation des auteurs : LASTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE/INFORMATIQUE/MATHEMATIQUE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans En ligne : https://hal.archives-ouvertes.fr/halshs-02283730 Format de la ressource électronique : vers HAL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95760 Geographical Modeling : Cities and Territories, ch. 4. Incremental Territorial Modeling / Clémentine Cottineau (2019)
Titre de série : Geographical Modeling : Cities and Territories, ch. 4 Titre : Incremental Territorial Modeling Type de document : Chapitre/Contribution Auteurs : Clémentine Cottineau, Auteur ; Paul Chapron , Auteur ; Marion Le Texier, Auteur ; Sébastien Rey-Coyrehourcq, Auteur Editeur : New York, Londres, Hoboken (New Jersey), ... : John Wiley & Sons Année de publication : 2019 Importance : pp 95 - 123 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] complexité
[Termes IGN] complexité de la carte
[Termes IGN] conception cartographique
[Termes IGN] modèle de simulation
[Termes IGN] représentation cartographique
[Termes IGN] territoireRésumé : (auteur) This chapter illustrates the importance of territorial representation by referring to a substrate closer to the discipline: cartographic modeling. It presents the main issues with embedding territorial representation and territorial dynamics in simulation models. The chapter depicts scientific practices and illustrates them with select model examples. It proposes a singular and reproducible modeling strategy, which aims specifically at describing a territorial system and its evolution. This strategy relies on multi‐modeling or incremental modeling. The chapter provides a presentation of the limits and opportunities of this approach, with a discussion of its applicability and interest to different case studies. It also provides the reader–modeler–geographer with a guide to represent territorial complexity in a progressive and well‐reasoned manner, in order to develop a reproducible territorial model and evaluation protocol. Numéro de notice : H2019-004 Affiliation des auteurs : LASTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE Nature : Chapître / contribution nature-HAL : ChOuvrScient DOI : 10.1002/9781119687290.ch4 Date de publication en ligne : 17/12/2019 En ligne : https://doi.org/10.1002/9781119687290.ch4 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95319 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])
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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
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Titre : Natural hazards Type de document : Monographie Auteurs : John P. Tiefenbacher, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2019 Importance : 276 p. Format : 19 x 27 cm ISBN/ISSN/EAN : 978-1-78984-086-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] barrage
[Termes IGN] distribution spatiale
[Termes IGN] environnement
[Termes IGN] gestion des risques
[Termes IGN] inondation
[Termes IGN] modèle de simulation
[Termes IGN] prévention des risques
[Termes IGN] risque naturel
[Termes IGN] risque technologique
[Termes IGN] sécurité nucléaire
[Termes IGN] séisme
[Termes IGN] surveillance d'ouvrage
[Termes IGN] viaducRésumé : (éditeur) Natural Hazards - Risk, Exposure, Response, and Resilience demonstrates advanced techniques to measure risks, exposures, responses, and solutions to hazards in an array of communities. Eleven original research reports by international scholars on hazard assessment and management are organized into four sections: studies assessing risk using in-depth modeling and technological detection to provide insight into problems associated with earthquakes, torrential rains, and nuclear power plant safety; studies revealing the spatial distributions of exposure and impacts from an assortment of hazards; studies examining human response to increased awareness of the patterns of hazard; and a study demonstrating assessment of resilience of sociotechnological systems to natural hazards. This volume contributes new conceptual and practical commentaries to assess, mitigate, and plan for disasters. Note de contenu : 1- Assessing seismic hazard in Chile using deep neural networks
2- Strong rainfall in Mato Grosso do Sul, Brazil: Synoptic analysis and numerical simulation
3- Natural hazards and nuclear power plant safety
4- Estimation of shear wave velocity profiles employing genetic algorithms and the diffuse field approach on microtremors array: Implications on liquefaction hazard at Port of Spain, Trinidad
5- Long-wave generation due to atmospheric-pressure variation and harbor oscillation in harbors of various shapes and countermeasures against meteotsunamis
6- Identification and assessment of hazard of development in gypsum karst regions: Examples from Turkey
7- Dam retirement and decision-making
8- Seismic hazard of viaduct transportation infrastructure
9- Determinants of coping strategies to floods and droughts in multiple geo-Ecological zones
10- Emergency communications network for disaster management
11- Interview of natural hazards anresilience of sociotechnological systems to natural hazardsNuméro de notice : 25944 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.77841 En ligne : https://doi.org/10.5772/intechopen.77841 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96333 PermalinkProjection sur l’évolution de la distribution future de la population en utilisant du Machine Learning et de la géosimulation / Julie Grosmaire (2019)PermalinkPermalinkDesigning an integrated urban growth prediction model: a scenario-based approach for preserving scenic landscapes / Sepideh Saeidi in Geocarto international, vol 33 n° 12 (December 2018)PermalinkLa filiera foresta-legno francese tra potenziale di mitigazione dei cambiamenti climatici e necessità di adattamento / Philippe Delacote in Agriregionieuropa, anno 14 n° 54 (2018)PermalinkFuzzy modelling of growth potential in forest development simulation / Damjan Strnad in Ecological Informatics, vol 48 (November 2018)PermalinkEstimation of forest above-ground biomass by geographically weighted regression and machine learning with Sentinel imagery / Lin Chen in Forests, vol 9 n° 10 (October 2018)PermalinkA new algorithm predicting the end of growth at five evergreen conifer forests based on nighttime temperature and the enhanced vegetation index / Huanhuan Yuan in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)PermalinkFine-grained prediction of urban population using mobile phone location data / Jie Chen in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)PermalinkEstimating storm damage with the help of low-altitude photographs and different sampling designs and estimators / Pekka Hyvönen in Silva fennica, vol 52 n° 3 ([01/08/2018])Permalink