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Modeling of precipitable water vapor from GPS observations using machine learning and tomography methods / Mir Reza Ghaffari Razin in Advances in space research, vol 69 n° 7 (April 2022)  

Public [article]  inAdvances in space research > vol 69 n° 7 (April 2022) . - pp 2671 - 2681 Titre : Modeling of precipitable water vapor from GPS observations using machine learning and tomography methods Type de document : Article/Communication Auteurs : Mir Reza Ghaffari Razin, Auteur ; Behzad Voosoghi, Auteur Année de publication : 2022 Article en page(s) : pp 2671 - 2681 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] algorithme génétique [Termes IGN] apprentissage automatique [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] Inférence floue [Termes IGN] Iran [Termes IGN] précipitation [Termes IGN] radiosondage [Termes IGN] réseau neuronal artificiel [Termes IGN] retard hydrostatique [Termes IGN] retard troposphérique zénithal [Termes IGN] tomographie par GPS [Termes IGN] vapeur d'eau [Termes IGN] voxel Résumé : (auteur) This paper studies the application of two machine learning methods to model precipitable water vapor (PWV) using observations of 23 GPS stations from the local GPS network of north-west of Iran in 2011. In a first step, the zenith tropospheric delay (ZTD) and zenith hydrostatic delay (ZHD) is calculated with the Bernese GNSS software and Saastamoinen model as revised by Davis, respectively. Then, by subtracting the ZHD from the ZTD, the zenith wet delay (ZWD) is obtained at each GPS station, for all times. In a second step, ZWD is modeled by two different machine learning methods, based on the latitude, longitude, DOY, time, relative humidity, temperature and pressure. After training a Support Vector Machine (SVM) and an Artificial Neural Network (ANN), ZWD temporal and spatial variations are estimated. Using the formula by Bevis, the ZWD can be converted to PWV at any time and space, for each machine learning method. The accuracy of the two new models is evaluated using control stations, exterior and radiosonde station, whose observations were not used in the training step. Also, all the results of the SVM and ANN are compared with a voxel-based tomography (VBT) model. In the control and exterior stations, ZWD estimated by the SVM (ZWDSVM) and ANN (ZWDANN) is compared with the ZWD obtained from the GPS (ZWDGPS). Also, in the control and exterior stations, precise point positioning (PPP) is used to evaluate the accuracy of the new models. In the radiosonde station, the PWV of the new models (PWVSVM, PWVANN) is compared with the radiosonde PWV (PWVradiosonde) and voxel-based PWV (PWVVBT). The averaged relative error of the SVM, ANN and VBT models in the control stations is 10.50%, 12.71% and 12.91%, respectively. For SVM, ANN and VBT models, the averaged RMSE at the control stations is 1.87 (mm), 2.22 (mm) and 2.29 (mm), respectively. Analysis of the results of PWV estimated by the SVM, ANN and VBT, as well as the surface precipitation obtained from meteorological stations, indicate the high accuracy of the SVM in comparison with the ANN and VBT model. In the results shown in this paper, the SVM has the best ability to accurately estimate ZWD and PWV, using local GPS network observations. Numéro de notice : A2022-446 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1016/j.asr.2022.01.003 Date de publication en ligne : 13/01/2022 En ligne : https://doi.org/10.1016/j.asr.2022.01.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100106 [article]

Spatiotemporal analysis of precipitable water vapor using ANFIS and comparison against voxel-based tomography and radiosonde / Mir Reza Ghaffari Razin in GPS solutions, vol 26 n° 1 (January 2022)  

Public [article]  inGPS solutions > vol 26 n° 1 (January 2022) . - n° 1 Titre : Spatiotemporal analysis of precipitable water vapor using ANFIS and comparison against voxel-based tomography and radiosonde Type de document : Article/Communication Auteurs : Mir Reza Ghaffari Razin, Auteur ; Samed Inyurt, Auteur Année de publication : 2022 Article en page(s) : n° 1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] Inférence floue [Termes IGN] modélisation spatio-temporelle [Termes IGN] positionnement ponctuel précis [Termes IGN] précipitation [Termes IGN] radiosondage [Termes IGN] retard troposphérique zénithal [Termes IGN] station GPS [Termes IGN] vapeur d'eau [Termes IGN] voxel Résumé : (auteur) Water vapor (WV) is one of the most important parameters in meteorological studies. Using an adaptive neuro-fuzzy inference system (ANFIS), a new method has been proposed for spatiotemporal modeling of precipitable WV (PWV). In a first step, the tropospheric zenith wet delay (ZWD) is calculated using the observations of 23 GPS stations in the northwest of Iran. Out of these 23 stations, 21 stations for training and 2 stations for testing and validating were selected. The observations are for 15 days, ranging from day of year (DOY) 300 to 314 in 2011. The reason for choosing this area and time interval is the availability of a complete set of data. Then, the values of ZWD are converted to PWV. The PWV values obtained from this step are considered as the output of the ANFIS. Also, the latitude and longitude values of the GPS stations, the DOY, observational time (min), temperature (T), pressure (P), and relative humidity (RH) are considered input to ANFIS. The ANFIS network is trained using the back-propagation algorithm. After the training step, the PWV values are evaluated at 2 test stations, KLBR and GGSH, and at Tabriz radiosonde station (38.08° N, 46.28°E). For a more accurate evaluation, all the results of the new method are compared with the voxel-based tomography model. The evaluation of the results is performed using the relative error, standard deviation, correlation coefficient, and root-mean-square error (RMSE). Also, precise point positioning (PPP) is used to better evaluate the proposed model at test stations. The value of the correlation coefficient at the radiosonde station for the ANFIS and voxel is 0.90 and 0.87, respectively. Also, the minimum RMSE calculated for the ANFIS and voxel are 1.02 and 1.06 mm, respectively. In the PPP analysis, an improvement of about 4 mm is observed in the coordinates of the test stations using ANFIS. The results confirm the capability and high accuracy of the proposed model in determining the temporal and spatial variations of PWV. Numéro de notice : A2022-003 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-021-01184-1 Date de publication en ligne : 19/10/2021 En ligne : https://doi.org/10.1007/s10291-021-01184-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98828 [article]

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 IGN] coefficient de corrélation [Termes IGN] données GPS [Termes IGN] erreur moyenne quadratique [Termes IGN] erreur relative [Termes IGN] Iran [Termes IGN] réfraction atmosphérique [Termes IGN] réseau neuronal artificiel [Termes IGN] retard troposphérique [Termes IGN] retard troposphérique zénithal [Termes IGN] tomographie par GPS [Termes IGN] vapeur d'eau [Termes 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 IGN] International Reference Ionosphere [Termes IGN] Iran [Termes IGN] modèle numérique [Termes IGN] ondelette [Termes IGN] réseau neuronal artificiel [Termes IGN] série temporelle [Termes 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]