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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 Identification of rainwater harvesting sites using SCS-CN methodology, remote sensing and Geographical Information System techniques / Tarun Kumar in Geocarto international, vol 32 n° 12 (December 2017)
[article]
Titre : Identification of rainwater harvesting sites using SCS-CN methodology, remote sensing and Geographical Information System techniques Type de document : Article/Communication Auteurs : Tarun Kumar, Auteur ; D. C. Jhariya, Auteur Année de publication : 2017 Article en page(s) : pp 1367 - 1388 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] aquifère
[Termes IGN] conservation des ressources naturelles
[Termes IGN] eau pluviale
[Termes IGN] eau souterraine
[Termes IGN] identification automatique
[Termes IGN] image satellite
[Termes IGN] Inde
[Termes IGN] ressources en eau
[Termes IGN] ruissellement
[Termes IGN] site
[Termes IGN] système d'information géographiqueRésumé : (Auteur) This study presents a method to identify potential sites for soil and water conservation techniques for the demarcation of suitable sites for artificial recharge of groundwater aquifers, in the study area. The run-off derived by the Soil Conservation Service Curve Number method is a function of run-off potential which can be expressed in terms of run-off coefficient. The augmentation of water resource is proposed by the construction of rainwater harvesting structures like check dam, percolation pond, farm pond and gully check dam. The site suitability for different water harvesting structures is determined by considering spatially varying parameters like slope, infiltration, run-off potential, landuse/land cover, stream order, soil texture, land capability class, hydrological soil group and micro-watershed area. The determined suitable site has been validated with existing recharge structures of the study area. Accuracy assessment of the suitable sites for recharge structures potential maps of the Bindra watershed is 82.60%. Numéro de notice : A2017-674 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1213772 En ligne : https://doi.org/10.1080/10106049.2016.1213772 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87177
in Geocarto international > vol 32 n° 12 (December 2017) . - pp 1367 - 1388[article]Effects of using different sources of remote sensing and geographic information system data on urban stormwater 2D–1D modeling / Yi Hong in Applied sciences, vol 7 n° 9 (September 2017)
[article]
Titre : Effects of using different sources of remote sensing and geographic information system data on urban stormwater 2D–1D modeling Type de document : Article/Communication Auteurs : Yi Hong, Auteur ; Céline Bonhomme, Auteur ; Bahman Soheilian , Auteur ; Ghassan Chebbo, Auteur Année de publication : 2017 Projets : TrafiPollu / Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] données lidar
[Termes IGN] données localisées 2D
[Termes IGN] données localisées 3D
[Termes IGN] eau pluviale
[Termes IGN] logiciel de simulation
[Termes IGN] modèle de simulation
[Termes IGN] modèle numérique de terrain
[Termes IGN] réseau d'assainissement
[Termes IGN] ruissellement
[Termes IGN] surface imperméable
[Termes IGN] utilisation du sol
[Termes IGN] ville
[Termes IGN] zone urbaineRésumé : (auteur) Remote sensing (RS) and geographic information system (GIS) data is increasingly used in urban stormwater modeling. The undirected use of such data may waste economic and human resources. In order to provide guidance for practitioners to efficiently use different data collection resources, as well as give a reference for future works, this paper aims to assess the effects of using free access GIS data and ad hoc RS data on urban 2D–1D stormwater modeling. The 2D-surface Two-dimensional Runoff, Erosion, and Export model (TREX) model was published in Science of the Total Environment in 2008. The 1D-sewer CANOE (Logiciel intégré de conception et de diagnostic des réseaux d’assainissement) model was published in Journal of Hydrology in 2004. The two models are integrated in the TRENOE (TREX-CANOE) platform. The modeling approach is applied to a small urban catchment near Paris (Le Perreux sur Marne, 0.12 km2). Simulation results reveal that the detailed land-use information derived from multiple data sources is a crucial factor for accurate simulations. Nevertheless, using the very high resolution LiDAR (light detection and ranging) data is not equally significant for the water flow simulations at sewage outlets. Finally, we suggest that using the free access GIS data accompanying the urban sewer network design might be an acceptable low-cost solution for accurate urban 2D–1D stormwater modeling during moderate rainfall events. Further studies of urban stormwater modeling could focus on the development of “suitable” models with “enough” input data, depending on the management/research objectives. Numéro de notice : A2017-842 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/app7090904 Date de publication en ligne : 05/09/2017 En ligne : https://doi.org/10.3390/app7090904 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89409
in Applied sciences > vol 7 n° 9 (September 2017)[article]Pit-mound microrelief in forest soils: Review of implications for water retention and hydrologic modelling / Martin Valtera in Forest ecology and management, vol 393 (1 June 2017)
[article]
Titre : Pit-mound microrelief in forest soils: Review of implications for water retention and hydrologic modelling Type de document : Article/Communication Auteurs : Martin Valtera, Auteur ; Randall J. Schaetzl, Auteur Année de publication : 2017 Article en page(s) : pp 40 - 51 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] écosystème forestier
[Termes IGN] gestion forestière durable
[Termes IGN] microtopographie
[Termes IGN] ruissellement
[Termes IGN] santé des forêts
[Termes IGN] sol forestier
[Termes IGN] stress hydrique
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Forest ecosystems are known for their capacity to retain and redistribute water. Nevertheless, even in some forested watersheds, prolonged or intense rainfall events often exceed the retention threshold of the system, generating accelerated runoff. Surface microrelief is an important attribute of forest ecosystems that often act to mediate potential runoff. In most natural forests, the soil surface is typically unevenly broken with pit and mound microrelief, formed by both historical and recent tree uprooting events. In managed forests, however, tree uprooting is traditionally seen as undesirable. The systematic repression of this process may lead to gradual loss of microrelief. To date, little attention has been paid to the impacts of the pit-mound microrelief, or its absence, on forest hydrology. Restoration of naturally undulating microrelief in managed forests can help to accentuate water retention and mitigate runoff, while reducing drought stress and reinforcing forest productivity and resilience.
This paper summarizes the literature and presents insights on the effects of tree uprooting on the microrelief of forest soils and forest hydrology, focusing on its consequences to water retention, tree water supply, and forest health. Furthermore, we explore the mechanisms and possible consequences of the long-term repression of these processes in intensively managed forests, with implications for forest management and further research.Numéro de notice : A2017-250 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2017.02.048 En ligne : https://doi.org/10.1016/j.foreco.2017.02.048 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85233
in Forest ecology and management > vol 393 (1 June 2017) . - pp 40 - 51[article]Analysis of spatial variability of near-surface soil moisture to increase rainfall-runoff modelling accuracy in SW Hungary / P. Hegedüs in Open geosciences, vol 7 n° 1 (January 2015)
[article]
Titre : Analysis of spatial variability of near-surface soil moisture to increase rainfall-runoff modelling accuracy in SW Hungary Type de document : Article/Communication Auteurs : P. Hegedüs, Auteur ; S. Czigány, Auteur ; E. Pirkhoffer, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 126 - 139 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] données localisées
[Termes IGN] Hongrie
[Termes IGN] humidité du sol
[Termes IGN] interpolation spatiale
[Termes IGN] modèle numérique
[Termes IGN] ruissellement
[Termes IGN] série temporelle
[Termes IGN] valléeRésumé : (auteur) Between September 5, 2008 and September 5, 2009, near-surface soil moisture time series were collected in the northern part of a 1.7 km2 watershed in SWHungary at 14 monitoring locations using a portable TDR-300 soil moisture sensor. The objectives of this study are to increase the accuracy of soil moisture measurement at watershed scale, to improve flood forecasting accuracy, and to optimize soil moisture sensor density.
According to our results, in 10 of 13 cases, a strong correlation exists between the measured soil moisture data of Station 5 and all other monitoring stations; Station 5 is considered representative for the entire watershed. Logically, the selection of the location of the representative measurement point(s) is essential for obtaining representative and accurate soil moisture values for the given watershed. This could be done by (i) employing monitoring stations of higher number at the exploratory phase of the monitoring, (ii) mapping soil physical properties at watershed scale, and (iii) running cross-relational statistical analyses on the obtained data.
Our findings indicate that increasing the number of soil moisture data points available for interpolation increases the accuracy of watershed-scale soil moisture estimation. The data set used for interpolation (and estimation of mean antecedent soil moisture values) could be improved (thus, having a higher number of data points) by selecting points of similar properties to the measurement points from the DEM and soil databases. By using a higher number of data points for interpolation, both interpolation accuracy and spatial resolution have increased for the measured soil moisture values for the Pósa Valley.Numéro de notice : A2015-438 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1515/geo-2015-0017 En ligne : https://doi.org/10.1515/geo-2015-0017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77038
in Open geosciences > vol 7 n° 1 (January 2015) . - pp 126 - 139[article]Data-driven feature learning for high resolution urban land-cover classification / Piotr Andrzej Tokarczyk (2015)PermalinkAccuracy assessment of MODIS/Terra snow cover product for parts of Indian Himalayas / Hari Prasad Chelamallu in Geocarto international, vol 29 n° 5 - 6 (August - October 2014)PermalinkTélédétection, SIG et modélisation de l'érosion hydrique dans le bassin versant de l'Oued Amzaz, Rif central / Jamal Chaaouan in Revue Française de Photogrammétrie et de Télédétection, n° 203 (Juillet 2013)PermalinkSnowSAR: Battling climate change for ESA / A. Meta in GEO: Geoconnexion international, vol 11 n° 8 (september 2012)PermalinkUse of high-resolution satellite imagery for investigating acid mine drainage from artisanal coal mining in North-Eastern India / B. Blahwar in Geocarto international, vol 27 n° 3 (June 2012)PermalinkClose range stereophotogrammetry and video imagery analyses in soil ecohydrology modelling / Maria J. Rossi in Photogrammetric record, vol 27 n° 137 (March - May 2012)PermalinkModelling the Zn emissions from roofing materials at Créteil city scale : Defining a methodology / Emna Sellami-Kaaniche (2012)PermalinkGIS for hydrological modelling / D. Kirkby ; K. Pegler ; David Coleman in GIM international, vol 25 n° 7 (July 2011)PermalinkAssessment of erosion, deposition and rill development on irregular soil surfaces using close range digital photogrammetry / G. Gessesse in Photogrammetric record, vol 25 n° 131 (September - November 2010)PermalinkEvaluation of a satellite-based global flood monitoring system / K. Yilmaz in International Journal of Remote Sensing IJRS, vol 31 n° 14 (July 2010)Permalink