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Precipitable water vapor fusion based on a generalized regression neural network / Bao Zhang in Journal of geodesy, vol 95 n° 4 (April 2021)
[article]
Titre : Precipitable water vapor fusion based on a generalized regression neural network Type de document : Article/Communication Auteurs : Bao Zhang, Auteur ; Yibing Yao, Auteur Année de publication : 2021 Article en page(s) : n° 36 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] Amérique du nord
[Termes IGN] coefficient d'étalonnage
[Termes IGN] coefficient de corrélation
[Termes IGN] données GNSS
[Termes IGN] données météorologiques
[Termes IGN] erreur systématique
[Termes IGN] fusion de données
[Termes IGN] image Aqua-MODIS
[Termes IGN] image Terra-MODIS
[Termes IGN] précipitation
[Termes IGN] prévision météorologique
[Termes IGN] régression
[Termes IGN] réseau neuronal artificiel
[Termes IGN] vapeur d'eau
[Termes IGN] variation temporelleRésumé : (auteur) Water vapor plays an important role in Earth’s weather and climate processes and energy transfer. Plenty of techniques have developed to monitor precipitable water vapor (PWV), but joint use of different techniques has some problems, including systematic biases, different spatiotemporal coverages and resolutions among different datasets. To address the above problems and improve the data utilization, we propose to use a generalized regression neural network (GRNN) to fuse PWVs from Global Navigation Satellite System (GNSS), Moderate-Resolution Imaging Spectroradiometer (MODIS), and European Centre for Medium‐Range Weather Forecasts Reanalysis 5 (ERA5). The core idea of this method is to use the high-quality GNSS PWV to calibrate and optimize the relatively low-quality MODIS and ERA5 PWV through the constructed GRNNs. Using the proposed method, we generated more than 400 PWV maps that combine GNSS, MODIS, and ERA5 PWVs in North America in 2018. Results show that the overall bias, standard deviation (STD), and root-mean-square (RMS) error are 0.0 mm, 2.1 mm, and 2.2 mm for the improved MODIS PWV, and 0.0 mm, 1.6 mm, and 1.6 mm for the improved ERA5 PWV. Compared to the original MODIS and ERA5 PWV, the total improvements are 37.1% and 15.8% in terms of RMS. The RMS improvements are mainly contributed from the calibration of bias for the MODIS PWV and optimization for the ERA5 PWV. It also demonstrates that the original MODIS PWV tends to be greater than the GNSS PWV while the ERA5 PWV has very small biases. After calibration and optimization, the correlation coefficients between the modified PWV and the GNSS PWV are 0.96 for the MODIS PWV and 0.98 for the ERA5 PWV. The proposed method also diminishes the temporal and spatial variations in accuracy, generating homogeneous PWV products. Since the biases among the three datasets are well removed and data accuracies are improved to the same level, they are thus easily fused and jointly used. Numéro de notice : A2021-259 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01482-z Date de publication en ligne : 01/03/2021 En ligne : https://doi.org/10.1007/s00190-021-01482-z Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97299
in Journal of geodesy > vol 95 n° 4 (April 2021) . - n° 36[article]Application of a multi-layer artificial neural network in a 3-D global electron density model using the long-term observations of COSMIC, Fengyun-3C, and Digisonde / Li Wang in Space weather, vol 19 n° 3 (March 2021)
[article]
Titre : Application of a multi-layer artificial neural network in a 3-D global electron density model using the long-term observations of COSMIC, Fengyun-3C, and Digisonde Type de document : Article/Communication Auteurs : Li Wang, Auteur ; Zhao Dongsheng ; Changyong He , Auteur ; et al., Auteur Année de publication : 2021 Projets : 3-projet - voir note / Article en page(s) : n° e2020SW002605 Note générale : bibliographie
The authors greatly appreciate the financial support from the National Natural Science Foundations of China (Grant No. 41730109, 41804013), the Natural Science Foundation of Jiangsu Province (Grant No. BK20200646, BK20200664), the Fundamental Re-search Funds for the Central Universi-ties (Grant No. 2020QN31, 2020QN30), the Project funded by China Postdoc-toral Science Foundation (Grant No. 2020M671645), the Open Fund of Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution (Grant No. KLSPWSEP-A06), A Project Funded by the Priority Academic Pro-gram Development of Jiangsu Higher Education Institutions (Surveying and Mapping).Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] image Formosat/COSMIC
[Termes IGN] modèle ionosphérique
[Termes IGN] Perceptron multicouche
[Termes IGN] réseau neuronal artificiel
[Termes IGN] teneur totale en électrons
[Termes IGN] variation saisonnièreRésumé : (auteur) The ionosphere plays an important role in satellite navigation, radio communication, and space weather prediction. However, it is still a challenging mission to develop a model with high predictability that captures the horizontal-vertical features of ionospheric electrodynamics. In this study, multiple observations during 2005–2019 from space-borne global navigation satellite system (GNSS) radio occultation (RO) systems (COSMIC and FY-3C) and the Digisonde Global Ionosphere Radio Observatory are utilized to develop a completely global ionospheric three-dimensional electron density model based on an artificial neural network, namely ANN-TDD. The correlation coefficients of the predicted profiles all exceed 0.96 for the training, validation and test datasets, and the minimum root-mean-square error of the predicted residuals is 7.8 × 104 el/cm3. Under quiet space weather, the predicted accuracy of the ANN-TDD is 30%–60% higher than the IRI-2016 at the Millstone Hill and Jicamarca incoherent scatter radars. However, the ANN-TDD is less capable of predicting ionospheric dynamic evolution under severe geomagnetic storms compared to the IRI-2016 with the STORM option activated. Additionally, the ANN-TDD successfully reproduces the large-scale horizontal-vertical ionospheric electrodynamic features, including seasonal variation and hemispheric asymmetries. These features agree well with the structure revealed by the RO profiles derived from the FORMOSAT/COSMIC-2 mission. Furthermore, the ANN-TDD successfully captures the prominent regional ionospheric patterns, including the equatorial ionization anomaly, Weddell Sea anomaly and mid-latitude summer nighttime anomaly. The new model is expected to play an important role in the application of GNSS navigation and in the explanation of the physical mechanisms involved. Numéro de notice : A2021-504 Affiliation des auteurs : ENSG+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1029/2020SW002605 Date de publication en ligne : 10/03/2021 En ligne : https://doi.org/10.1029/2020SW002605 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99369
in Space weather > vol 19 n° 3 (March 2021) . - n° e2020SW002605[article]Machine learning in ground motion prediction / Farid Khosravikia in Computers & geosciences, vol 148 (March 2021)
[article]
Titre : Machine learning in ground motion prediction Type de document : Article/Communication Auteurs : Farid Khosravikia, Auteur ; Patricia Clayton, Auteur Année de publication : 2021 Article en page(s) : n° 104700 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] Etats-Unis
[Termes IGN] modèle de régression
[Termes IGN] modèle de simulation
[Termes IGN] mouvement de terrain
[Termes IGN] régression linéaire
[Termes IGN] réseau neuronal artificiel
[Termes IGN] sismicitéRésumé : (auteur) This paper studies the advantages and disadvantages of different machine learning techniques in predicting ground-motion intensity measures given source characteristics, source-to-site distance, and local site conditions. Typically, linear regression-based models with predefined equations and coefficients are used in ground motion prediction. However, restrictions of the linear regression models may limit their capabilities in extracting complex nonlinear behaviors in the data. Therefore, the present paper comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. This study quantifies event-to-event and site-to-site variability of the ground motions by implementing them as random effect terms to reduce the aleatory uncertainty. All the algorithms are trained using a selected database of 4528 ground-motions, including 376 seismic events with magnitude 3 to 5.8, recorded over the hypocentral distance range of 4–500 km in Oklahoma, Kansas, and Texas since 2005. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring predefined equations or coefficients. Moreover, it is found that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Numéro de notice : A2021-230 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/POSITIONNEMENT Nature : Article DOI : 10.1016/j.cageo.2021.104700 Date de publication en ligne : 21/01/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104700 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97220
in Computers & geosciences > vol 148 (March 2021) . - n° 104700[article]Multi-level progressive parallel attention guided salient object detection for RGB-D images / Zhengyi Liu in The Visual Computer, vol 37 n° 3 (March 2021)
[article]
Titre : Multi-level progressive parallel attention guided salient object detection for RGB-D images Type de document : Article/Communication Auteurs : Zhengyi Liu, Auteur ; Quntao Duan, Auteur ; Song Shi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 529 - 540 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] approche hiérarchique
[Termes IGN] détection automatique
[Termes IGN] détection d'objet
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image RVB
[Termes IGN] optimisation spatiale
[Termes IGN] profondeur
[Termes IGN] réseau neuronal récurrent
[Termes IGN] saillanceRésumé : (auteur) Detecting salient objects in RGB-D images attracts more and more attention in recent years. It benefits from the widespread use of depth sensors and can be applied in the comprehensive understanding of RGB-D images. Existing models focus on double-stream networks which transfer from color stream to depth stream, but depth stream with one channel information cannot learn the same feature as color stream with three channels information even if HHA representation is adopted. In our works, RGB-D four-channels input is chosen, and meanwhile, progressive parallel spatial and channel attention mechanisms are performed to improve feature representation. Spatial and channel attention can pay more attention on partial positions and channels in the image which show higher response to salient objects. Both attentive features are optimized by attentive feature from higher layer, respectively, and parallel fed into recurrent convolutional layer to generate side-output saliency maps guided by saliency map from higher layer. Last multi-level saliency maps are fused together from multi-scale perspective. Experiments on benchmark datasets demonstrate that parallel attention mechanism and progressive optimization operation play an important role in improving the accuracy of salient object detection, and our model outperforms state-of-the-art models in evaluation matrices. Numéro de notice : A2021-340 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01821-9 Date de publication en ligne : 18/02/2020 En ligne : https://doi.org/10.1007/s00371-020-01821-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97578
in The Visual Computer > vol 37 n° 3 (March 2021) . - pp 529 - 540[article]Suitability assessment of urban land use in Dalian, China using PNN and GIS / Ziqian Kang in Natural Hazards, vol 106 n° 1 (March 2021)
[article]
Titre : Suitability assessment of urban land use in Dalian, China using PNN and GIS Type de document : Article/Communication Auteurs : Ziqian Kang, Auteur ; Shuo Wang, Auteur ; Ling Xu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 913 - 936 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] aire naturelle (écologie)
[Termes IGN] analyse multicritère
[Termes IGN] bâtiment industriel
[Termes IGN] Chine
[Termes IGN] classificateur paramétrique
[Termes IGN] distribution spatiale
[Termes IGN] habitat urbain
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] système d'information géographique
[Termes IGN] utilisation du sol
[Termes IGN] zone urbaineRésumé : (auteur) The suitability assessment of land use is crucial to avoid wasting land resources. However, the traditional methods with subjective weights are prone to reduce the reasonability and reliability of assessment. For filling this knowledge gap, the probability neural network (PNN) coupled with GIS was adopted to evaluate the land use suitability in this paper. According to the applications of the urban land resource, the land use was divided into three types (resident, industry and ecological reserve). Thus, the three different assessment criteria systems were built for the three land use types. The result of residential land use indicated that the most suitable, suitable and normal suitable residential land were 401, 272 and 12,406 km2 and mainly located in Changhai, Lvshun and Pulandian accordingly. The most suitable land for industry was in Ganjingzi, Jinzhou and Wafangdian and accounted for 22% of the total area. While the most suitable land for ecological reserve was in Pulandian and Zhuanghe with the area of 1967 km2. The results indicated that the south of Dalian was suitable for the residential land use, north of Dalian was suitable for the ecological land use and the central was suitable for industrial land use. The results were coincided to the actual spatial distribution of land use. The proposed PNN coupled with GIS assessment method in suitability of land use is conducted to provide a more reasonable assessment result that can be used by managers and regulators. Numéro de notice : A2021-419 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s11069-020-04500-z Date de publication en ligne : 04/01/2021 En ligne : https://doi.org/10.1007/s11069-020-04500-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97769
in Natural Hazards > vol 106 n° 1 (March 2021) . - pp 913 - 936[article]Coastal water remote sensing from sentinel-2 satellite data using physical, statistical, and neural network retrieval approach / Frank S. Marzano in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)PermalinkA comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping / Zhice Fang in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)PermalinkCrop identification by massive processing of multiannual satellite imagery for EU common agriculture policy subsidy control / Adolfo Lozano-Tello in European journal of remote sensing, vol 54 n° 1 (2021)PermalinkPermalinkPermalinkApprentissage profond et IA pour l’amélioration de la robustesse des techniques de localisation par vision artificielle / Achref Elouni (2021)PermalinkPermalinkPermalinkPermalinkPermalink