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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