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Hourly rainfall forecast model using supervised learning algorithm / Qingzhi Zhao in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 1 (January 2022) . - n° 4100509 Titre : Hourly rainfall forecast model using supervised learning algorithm Type de document : Article/Communication Auteurs : Qingzhi Zhao, Auteur ; Yang Liu, Auteur ; Wanqiang Yao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 4100509 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] autocorrélation [Termes IGN] classification dirigée [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] données GNSS [Termes IGN] heure [Termes IGN] modèle de simulation [Termes IGN] modèle météorologique [Termes IGN] précipitation [Termes IGN] série temporelle [Termes IGN] station GNSS [Termes IGN] Taïwan [Termes IGN] vapeur d'eau Résumé : (auteur) Previous studies on short-term rainfall forecast using precipitable water vapor (PWV) and meteorological parameters mainly focus on rain occurrence, while the rainfall forecast is rarely investigated. Therefore, an hourly rainfall forecast (HRF) model based on a supervised learning algorithm is proposed in this study to predict rainfall with high accuracy and time resolution. Hourly PWV derived from Global Navigation Satellite System (GNSS) and temperature data are used as input parameters of the HRF model, and a support vector machine is introduced to train the proposed model. In addition, this model also considers the time autocorrelation of rainfall in the previous epoch. Hourly PWV data of 21 GNSS stations and collocated meteorological parameters (temperature and rainfall) for five years in Taiwan Province are selected to validate the proposed model. Internal and external validation experiments have been performed under the cases of slight, moderate, and heavy rainfall. Average root-mean-square error (RMSE) and relative RMSE of the proposed HRF model are 1.36/1.39 mm/h and 1.00/0.67, respectively. In addition, the proposed HRF model is compared with the similar works in previous studies. Compared results reveal the satisfactory performance and superiority of the proposed HRF model in terms of time resolution and forecast accuracy. Numéro de notice : A2022-024 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2021.3054582 Date de publication en ligne : 09/02/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3054582 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99253 [article]

Global ionosphere maps based on GNSS, satellite altimetry, radio occultation and DORIS / Peng Chen in GPS solutions, vol 21 n° 2 (April 2017)  

Public [article]  inGPS solutions > vol 21 n° 2 (April 2017) . - pp 639 – 650 Titre : Global ionosphere maps based on GNSS, satellite altimetry, radio occultation and DORIS Type de document : Article/Communication Auteurs : Peng Chen, Auteur ; Yi Bin Yao, Auteur ; Wanqiang Yao, Auteur Année de publication : 2017 Article en page(s) : pp 639 – 650 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] coordonnées GNSS [Termes IGN] données altimétriques [Termes IGN] données DORIS [Termes IGN] ionosphère [Termes IGN] modèle ionosphérique [Termes IGN] occultation du signal [Termes IGN] radiooccultation [Termes IGN] teneur verticale totale en électrons Résumé : (auteur) Global ionosphere maps (GIMs) provided by the global navigation satellite systems (GNSS) data are essential in ionospheric research as the source of the global vertical total electron content (VTEC). However, conventional GIMs experience lower accuracy and reliability from uneven distribution of GNSS tracking stations, especially in ocean areas with few tracking stations. The orbits of ocean altimetry satellite cover vast ocean areas and can directly provide VTEC at nadir with two different wavelengths of radio waves. Radio occultation observations and the beacons of Doppler orbitography and radio positioning integrated by satellite (DORIS) are evenly distributed globally. Satellite altimetry, radio occultation and DORIS can compensate GNSS data in ocean areas, allowing a more accurate and reliable GIMs to be formed with the integration of these observations. This study builds GIMs with temporal intervals of 2 h by the integration of GNSS, satellite altimetry, radio occultation and DORIS data. We investigate the integration method for multi-source data and used the data in May 2013 to validate the effectiveness of integration. Result shows that VTEC changes by −11.0 to −7.0 TECU after the integration of satellite altimetry, radio occultation and DORIS data. The maximum root mean square decreases by 5.5 TECU, and the accuracy of GIMs in ocean areas improves significantly. Numéro de notice : A2017-216 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-016-0554-9 En ligne : http://dx.doi.org/10.1007/s10291-016-0554-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85089 [article]