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Optimal lowest astronomical tide estimation using maximum likelihood estimator with multiple ocean models hybridization / Mohammed El-Diasty in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)
[article]
Titre : Optimal lowest astronomical tide estimation using maximum likelihood estimator with multiple ocean models hybridization Type de document : Article/Communication Auteurs : Mohammed El-Diasty, Auteur Année de publication : 2020 Article en page(s) : 11 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Océanographie
[Termes IGN] carte marine
[Termes IGN] données hydrographiques
[Termes IGN] incertitude des données
[Termes IGN] levé hydrographique
[Termes IGN] marée océanique
[Termes IGN] marégraphe
[Termes IGN] méthode du maximum de vraisemblance (estimation)
[Termes IGN] modèle océanographique
[Termes IGN] navigation maritime
[Termes IGN] niveau de la mer
[Termes IGN] océanographie dynamique
[Termes IGN] Rouge, merRésumé : (auteur) Developing an accurate Lowest Astronomical Tide (LAT) in a continuous form is essential for many maritime applications as it can be employed to develop an accurate continuous vertical control datum for hydrographic surveys applications and to produce accurate dynamic electronic navigation charts for safe maritime navigation by mariners. The LAT can be developed in a continuous (surface) using an estimated LAT surface model from the hydrodynamic ocean model along with coastal discrete LAT point values derived from tide gauges data sets to provide the corrected LAT surface model. In this paper, an accurate LAT surface model was developed for the Red Sea case study using a Maximum Likelihood Estimator (MLE) with multiple hydrodynamic ocean models hybridization, namely, WebTide, FES2014, DTU10, and EOT11a models. It was found that the developed optimal hybrid LAT model using MLE with multiple hydrodynamic ocean models hybridization ranges from 0.1 m to 1.63 m, associated with about 2.4 cm of uncertainty at a 95% confidence level in the Red Sea case study area. To validate the accuracy of the developed model, the comparison was made between the optimal hybrid LAT model developed from multiple hydrodynamic ocean models hybridization using the MLE method with the individual LAT models estimated from individual WebTide, FES2014, DTU10, or EOT11a ocean models based on the associated uncertainties estimated at a 95% confidence level. It was found that the optimal hybrid LAT model accuracy is superior to the individual LAT models estimated from individual ocean models with an improvement of about 50% in average, based on the estimated uncertainties. The importance of developing optimal LAT surface model using the MLE method with multiple hydrodynamic ocean models hybridization in this paper with few centimeters level of uncertainty can lead to accurate continuous vertical datum estimation that is essential for many maritime applications. Numéro de notice : A2020-301 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9050327 Date de publication en ligne : 17/05/2020 En ligne : https://doi.org/10.3390/ijgi9050327 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95141
in ISPRS International journal of geo-information > vol 9 n° 5 (May 2020) . - 11 p.[article]Wavelet-adaptive neural subtractive clustering fuzzy inference system to enhance low-cost and high-speed INS/GPS navigation system / Elahe S. Abdolkarimi in GPS solutions, vol 24 n° 2 (April 2020)
[article]
Titre : Wavelet-adaptive neural subtractive clustering fuzzy inference system to enhance low-cost and high-speed INS/GPS navigation system Type de document : Article/Communication Auteurs : Elahe S. Abdolkarimi, Auteur ; Mohammad-Reza Mosavi, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] centrale inertielle
[Termes IGN] coût
[Termes IGN] filtre de Kalman
[Termes IGN] GPS-INS
[Termes IGN] imprécision des données
[Termes IGN] incertitude des données
[Termes IGN] Inférence floue
[Termes IGN] précision du positionnement
[Termes IGN] rapport signal sur bruit
[Termes IGN] transformation en ondelettes
[Termes IGN] vitesse de déplacementRésumé : (auteur) The combined navigation system consisting of Global Positioning System (GPS) and Inertial Navigation System in a complementary mode assures an accurate, reliable, and continuous positioning capability in the navigation system. Because of problems such as dealing with a low-cost MEMS-based inertial sensors having a high level of uncertainty and imprecision, stochastic noise, a high-speed vehicle, high noisy real data, and long-term GPS signal outage during the real-time flight test, the advantage is taken for some approaches in different steps: (1) utilizing discrete wavelet transform technique to enhance the signal-to-noise ratio in raw and noisy inertial sensor signals and attenuate high-frequency noise as a preprocessing phase to prepare more accurate data for the proposed model and (2) employing adaptive neural subtractive clustering fuzzy inference system (ANSCFIS) which combines and extracts the best feature of adaptive neuro-fuzzy inference system (ANFIS), and the subtractive clustering algorithm with fewer rules than the ANFIS method, aiming to improve a more efficient, accurate, and especially a faster method which enhances the prediction accuracy and speeds up the positioning system. The achieved accuracies for the proposed model are discussed and compared with the extended Kalman filter (EKF), ANFIS, and ANSCFIS which are implemented and tested experimentally using a high-speed vehicle in three GPS blockages. The proposed model shows considerable improvements in high-speed navigation using low-cost MEMS-based inertial sensors in case of long-term GPS blockage. Numéro de notice : A2020-084 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-0951-y Date de publication en ligne : 11/01/2020 En ligne : https://doi.org/10.1007/s10291-020-0951-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94654
in GPS solutions > vol 24 n° 2 (April 2020)[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)
[article]
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
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 2 (February 2020) . - pp[article]Uncertainty analysis of remotely-acquired thermal infrared data to extract the thermal Properties of active lava surfaces / James A. Thompson in Remote sensing, vol 12 n° 1 (January 2020)
[article]
Titre : Uncertainty analysis of remotely-acquired thermal infrared data to extract the thermal Properties of active lava surfaces Type de document : Article/Communication Auteurs : James A. Thompson, Auteur ; Michael S. Ramsey, Auteur Année de publication : 2020 Article en page(s) : 21 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Advanced Spaceborne Thermal Emission and Reflection Radiometer
[Termes IGN] classification pixellaire
[Termes IGN] éruption volcanique
[Termes IGN] image MASTER
[Termes IGN] image thermique
[Termes IGN] incertitude des données
[Termes IGN] Kilauea (volcan)
[Termes IGN] lave
[Termes IGN] rayonnement infrarouge thermique
[Termes IGN] surveillance géologique
[Termes IGN] température
[Termes IGN] volcanRésumé : (auteur) Using thermal infrared (TIR) data from multiple instruments and platforms for analysis of an entire active volcanic system is becoming more common with the increasing availability of new data. However, the accuracy and uncertainty associated with these combined datasets are poorly constrained over the full range of eruption temperatures and possible volcanic products. Here, four TIR datasets acquired over active lava surfaces are compared to quantify the uncertainty, accuracy, and variability in derived surface radiance, emissivity, and kinetic temperature. These data were acquired at Kīlauea volcano in Hawai’i, USA, in January/February 2017 and 2018. The analysis reveals that spatial resolution strongly limits the accuracy of the derived surface thermal properties, resulting in values that are significantly below the expected values for molten basaltic lava at its liquidus temperature. The surface radiance is ~2400% underestimated in the orbital data compared to only ~200% in ground-based data. As a result, the surface emissivity is overestimated and the kinetic temperature is underestimated by at least 30% and 200% in the airborne and orbital datasets, respectively. A thermal mixed pixel separation analysis is conducted to extract only the molten fraction within each pixel in an attempt to mitigate this complicating factor. This improved the orbital and airborne surface radiance values to within 15% of the expected values and the derived emissivity and kinetic temperature within 8% and 12%, respectively. It is, therefore, possible to use moderate spatial resolution TIR data to derive accurate and reliable emissivity and kinetic temperatures of a molten lava surface that are comparable to the higher resolution data from airborne and ground-based instruments. This approach, resulting in more accurate kinetic temperature and emissivity of the active surfaces, can improve estimates of flow hazards by greatly improving lava flow propagation models that rely on these data. Numéro de notice : A2020-224 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs12010193 Date de publication en ligne : 05/01/2020 En ligne : https://doi.org/10.3390/rs12010193 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94939
in Remote sensing > vol 12 n° 1 (January 2020) . - 21 p.[article]Spatially-explicit sensitivity and uncertainty analysis in a MCDA-based flood vulnerability model / Mariana Madruga de bruto in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)
[article]
Titre : Spatially-explicit sensitivity and uncertainty analysis in a MCDA-based flood vulnerability model Type de document : Article/Communication Auteurs : Mariana Madruga de bruto, Auteur ; Adrian Almoradie, Auteur ; Mariele Evers, Auteur Année de publication : 2019 Article en page(s) : pp 1788-1806 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de sensibilité
[Termes IGN] analyse multicritère
[Termes IGN] Brésil
[Termes IGN] Geospatial data abstraction library
[Termes IGN] incertitude des données
[Termes IGN] inondation
[Termes IGN] méthode robuste
[Termes IGN] modèle de simulation
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] Python (langage de programmation)
[Termes IGN] vulnérabilité
[Termes IGN] zone à risqueRésumé : (auteur) This study presents a methodology for conducting sensitivity and uncertainty analysis of a GIS-based multi-criteria model used to assess flood vulnerability in a case study in Brazil. The paper explores the robustness of model outcomes against slight changes in criteria weights. One criterion was varied at-a-time, while others were fixed to their baseline values. An algorithm was developed using Python and a geospatial data abstraction library to automate the variation of weights, implement the ANP (analytic network process) tool, reclassify the raster results, compute the class switches, and generate an uncertainty surface. Results helped to identify highly vulnerable areas that are burdened by high uncertainty and to investigate which criteria contribute to this uncertainty. Overall, the criteria ‘houses with improper building material’ and ‘evacuation drills and training’ are the most sensitive ones, thus, requiring more accurate measurements. The sensitivity of these criteria is explained by their weights in the base run, their spatial distribution, and the spatial resolution. These findings can support decision makers to characterize, report, and mitigate uncertainty in vulnerability assessment. The case study results demonstrate that the developed approach is simple, flexible, transparent, and may be applied to other complex spatial problems. Numéro de notice : A2019-389 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1599125 Date de publication en ligne : 05/04/2019 En ligne : https://doi.org/10.1080/13658816.2019.1599125 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93480
in International journal of geographical information science IJGIS > vol 33 n° 9 (September 2019) . - pp 1788-1806[article]Réservation
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