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Estimation of tropospheric wet refractivity using tomography method and artificial neural networks in Iranian case study / Mir Reza Ghaffari Razin in GPS solutions, Vol 24 n° 3 (July 2020)  

Public [article]  inGPS solutions > Vol 24 n° 3 (July 2020) Titre : Estimation of tropospheric wet refractivity using tomography method and artificial neural networks in Iranian case study Type de document : Article/Communication Auteurs : Mir Reza Ghaffari Razin, Auteur ; Behzad Voosoghi, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes descripteurs IGN] coefficient de corrélation [Termes descripteurs IGN] données GPS [Termes descripteurs IGN] erreur moyenne quadratique [Termes descripteurs IGN] erreur relative [Termes descripteurs IGN] Iran [Termes descripteurs IGN] réfraction atmosphérique [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] retard troposphérique [Termes descripteurs IGN] retard troposphérique zénithal [Termes descripteurs IGN] tomographie par GPS [Termes descripteurs IGN] vapeur d'eau [Termes descripteurs IGN] voxel Résumé : (auteur) Using the observations from local and regional GPS networks, the estimation of slant wet delays (SWDs) is possible for each line of sight between satellite and receiver. The observations of SWD are used to model horizontal and vertical variations of the wet refractivity in the atmosphere above the study area. This work is done using the tomography method. In tomography, the horizontal variations of tropospheric wet refractivity are modeled with the polynomial in degree and rank of 2 with latitude and longitude as variables. Also, altitude variations are modeled in the form of discrete layers with constant heights. The main innovation is to estimate the tropospheric parameters for each line of sight by the artificial neural networks (ANNs). The SWD obtained from GPS observations for the different signals at each station is compared with the SWD generated by the ANNs (SWDGPS–SWDANNs). The square of the difference between these two values is introduced as the cost function in the ANNs. To evaluate, we used observations from October 27 to 31, 2011. The availability of GPS and radiosonde data is the main reason for choosing this timeframe. The correlation coefficient, root mean square error (RMSE), and relative error allow for evaluation of the proposed model. The results were also compared with the results of the voxel-based troposphere tomography method. For a more detailed evaluation, four test stations are selected and ANN zenith wet delays (ZWDANN) are compared with the ZWDGPS. Observations of test stations are not used in the modeling step. The correlation coefficient in the testing step for TomoANN and Tomovoxel is 0.9006 and 0.8863, respectively. The mean RMSE at 5 days for TomoANN and Tomovoxel is calculated as 0.63 and 0.71 mm/km, respectively. Also, the average relative error at the four test stations for TomoANN is 15.37% and for Tomovoxel it is 19.69%. The results demonstrate the better capability of the proposed method in the modeling of the tropospheric wet refractivity in the region of Iran. Numéro de notice : A2020-238 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-00979-y date de publication en ligne : 10/04/2020 En ligne : https://doi.org/10.1007/s10291-020-00979-y Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94986 [article]

Modeling of ionosphere time series using wavelet neural networks (case study: N-W of Iran) / Mir Reza Ghaffari Razin in Advances in space research, vol 58 n° 1 (July 2016)  

Public [article]  inAdvances in space research > vol 58 n° 1 (July 2016) . - pp 74 - 83 Titre : Modeling of ionosphere time series using wavelet neural networks (case study: N-W of Iran) Type de document : Article/Communication Auteurs : Mir Reza Ghaffari Razin, Auteur ; Behzad Voosoghi, Auteur Année de publication : 2016 Article en page(s) : pp 74 - 83 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes descripteurs IGN] International Reference Ionosphere [Termes descripteurs IGN] Iran [Termes descripteurs IGN] modèle numérique [Termes descripteurs IGN] ondelette [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] teneur totale en électrons Résumé : (auteur) Wavelet neural networks (WNNs) are important tools for analyzing time series especially when it is non-linear and non-stationary. It takes advantage of high resolution of wavelets and feed forward nature of neural networks (NNs). Therefore, in this paper, WNNs is used for modeling of ionosphere time series in Iran. To apply the method, observations collected at 22 GPS stations in 12 successive days of 2012 (DOY# 219–230) from Azerbaijan local GPS network are used. For training of WNN, back-propagation (BP) algorithm is used. The results of WNN compared with results of international reference ionosphere 2012 (IRI-2012) and international GNSS service (IGS) products. To assess the error of WNN, statistical indicators, relative and absolute errors are used. Minimum relative error for WNN compared with GPS TEC is 6.37% and maximum relative error is 12.94%. Also the maximum and minimum absolute error computed 6.32 and 0.13 TECU, respectively. Comparison of diurnal predicted TEC values from the WNN model and the IRI-2012 with GPS TEC revealed that the WNN provides more accurate predictions than the IRI-2012 model and IGS products in the test area. Numéro de notice : A2016-562 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1016/j.asr.2016.04.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81742 [article]