Hong Sheng Lee, Wan Anom Wan Aris, Tajul Ariffin Musa, Ahmad Zuri Sha’ameri, Ooi Wei Han, Dong-Ha Lee, Mohammad Asrul Mustafar
Abstract The study introduces the concept of regional GPS orbit determination, whereby GPS satellite positions are determined using GPS measurements from locally or regional distributed stations. The importance and characteristics of regional GPS orbit are briefly discussed. The technique used to determine the regional GPS satellite position is coined Inverse Single Point Positioning (ISPP). Code-based pseudorange is used and improved using residual correction model. Two designs of station distribution are selected in this study, which only cover stations in Malaysia and stations situated 8000 km from a reference point in Malaysia. The root-mean-squared-error (RMSE) of ISPP when compared against final ephemeris were 660.65 m and 27.61 m, while the 3D RMSE of positioning were 1.612 m and 1.324 m for the first and second designs, respectively, lower than the accuracy of broadcast ephemeris. Three parameters are identified as factors affecting accuracy of ISPP, namely geometry of station distribution, nature of measurement used, and technique of orbit determination. Further research will be required to fully realize a functional regional GPS orbit.
{"title":"Regional GPS orbit determination using code-based pseudorange measurement with residual correction model","authors":"Hong Sheng Lee, Wan Anom Wan Aris, Tajul Ariffin Musa, Ahmad Zuri Sha’ameri, Ooi Wei Han, Dong-Ha Lee, Mohammad Asrul Mustafar","doi":"10.1515/jag-2023-0044","DOIUrl":"https://doi.org/10.1515/jag-2023-0044","url":null,"abstract":"Abstract The study introduces the concept of regional GPS orbit determination, whereby GPS satellite positions are determined using GPS measurements from locally or regional distributed stations. The importance and characteristics of regional GPS orbit are briefly discussed. The technique used to determine the regional GPS satellite position is coined Inverse Single Point Positioning (ISPP). Code-based pseudorange is used and improved using residual correction model. Two designs of station distribution are selected in this study, which only cover stations in Malaysia and stations situated 8000 km from a reference point in Malaysia. The root-mean-squared-error (RMSE) of ISPP when compared against final ephemeris were 660.65 m and 27.61 m, while the 3D RMSE of positioning were 1.612 m and 1.324 m for the first and second designs, respectively, lower than the accuracy of broadcast ephemeris. Three parameters are identified as factors affecting accuracy of ISPP, namely geometry of station distribution, nature of measurement used, and technique of orbit determination. Further research will be required to fully realize a functional regional GPS orbit.","PeriodicalId":45494,"journal":{"name":"Journal of Applied Geodesy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136262112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The paper presents monitoring of spatial displacements and deformation of hydraulic structures of hydroelectric power plants of the Dnipro and Dnister cascades using permanent deformation monitoring systems (PDMS). The architecture of the geodetic (measuring) component of monitoring systems deployed at Kaniv, Dnipro, Seredniodniprovska and Dnister HPP (all in Ukraine) are presented, as well as deformation monitoring structure of this systems. Analysis of the impact of geodynamic and seismic factors on the operation of selected monitoring systems are presented. The spatial deformations of the base points on all the studied monitoring systems were determined. As a result of the analysis of the obtained values, it was found that the deformations of the dam crest have a seasonal nature (with half-annual period) of movements, and are characterized by the corresponding vector field and the absolute value of spatial movements.
{"title":"Monitoring of spatial displacements and deformation of hydraulic structures of hydroelectric power plants of the Dnipro and Dnister cascades (Ukraine)","authors":"Korneliy Tretyak, Yuriy Bisovetskyi, Ihor Savchyn, Tetiana Korlyatovych, Oleg Chernobyl, Sergey Kukhtarov","doi":"10.1515/jag-2023-0021","DOIUrl":"https://doi.org/10.1515/jag-2023-0021","url":null,"abstract":"Abstract The paper presents monitoring of spatial displacements and deformation of hydraulic structures of hydroelectric power plants of the Dnipro and Dnister cascades using permanent deformation monitoring systems (PDMS). The architecture of the geodetic (measuring) component of monitoring systems deployed at Kaniv, Dnipro, Seredniodniprovska and Dnister HPP (all in Ukraine) are presented, as well as deformation monitoring structure of this systems. Analysis of the impact of geodynamic and seismic factors on the operation of selected monitoring systems are presented. The spatial deformations of the base points on all the studied monitoring systems were determined. As a result of the analysis of the obtained values, it was found that the deformations of the dam crest have a seasonal nature (with half-annual period) of movements, and are characterized by the corresponding vector field and the absolute value of spatial movements.","PeriodicalId":45494,"journal":{"name":"Journal of Applied Geodesy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136293213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Satellite-based navigation systems are widely used in transportation. GNSS signal’s strength or quality can easily be degraded by local environments. As a result, the position accuracy of satellite-based navigation systems decreases. In this paper, a novel approach for estimating the positioning error is proposed using ML/DL technique. For learning the relationship between position errors and increased data from GNSS receivers without any prior experience, neural networks have become the machine learning option of choice in the past few years. Signal degradation is best measured by dilution of precision, elevation angles, and carrier-to-noise ratios. To estimate the position error of satellite-based navigation systems, neural networks are trained in this paper. This paper applies a long-short-term memory (LSTM) network to model the temporal correlation of position error measurements. Therefore, neural networks are capable of learning the trend of position errors through training.
{"title":"A machine-learning approach to estimate satellite-based position errors","authors":"Anil Kumar Ramavath, Naveen Kumar Perumalla","doi":"10.1515/jag-2023-0051","DOIUrl":"https://doi.org/10.1515/jag-2023-0051","url":null,"abstract":"Abstract Satellite-based navigation systems are widely used in transportation. GNSS signal’s strength or quality can easily be degraded by local environments. As a result, the position accuracy of satellite-based navigation systems decreases. In this paper, a novel approach for estimating the positioning error is proposed using ML/DL technique. For learning the relationship between position errors and increased data from GNSS receivers without any prior experience, neural networks have become the machine learning option of choice in the past few years. Signal degradation is best measured by dilution of precision, elevation angles, and carrier-to-noise ratios. To estimate the position error of satellite-based navigation systems, neural networks are trained in this paper. This paper applies a long-short-term memory (LSTM) network to model the temporal correlation of position error measurements. Therefore, neural networks are capable of learning the trend of position errors through training.","PeriodicalId":45494,"journal":{"name":"Journal of Applied Geodesy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135304071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The Ionospheric Total Electron Content (TEC) measured in the ray path of the signals directly contributes to the Range Error (RE) of the satellite signals, which affects positioning and navigation. Employing the Co-Kriging-based Surrogate Model (COKSM) to predict TEC and RE correction has proven prolific. This research attempted to test and compare the prediction capability of COKSM with an Artificial Intelligence-based Feed Forward Neural Network model (FFNN) during five X-Class Solar Flares of 2021–22. Also, the results are validated by comparing them with the IRI PLAS 2017 model. TEC, solar, and geomagnetic parameters data for Hyderabad GPS station located at 17.31° N latitude and 78.55° E longitude were collected from IONOLAB & OMNIWEB servers. The COKSM uses six days of input data to predict the 7th day TEC, whereas prediction using the FFNN model is done using 45 days of data before the prediction date. The performance evaluation is done using RMSE, NRMSE, Correlation Coefficient, and sMAPE. The average RMSE for COKSM varied from 1.9 to 9.05, for FFNN it varied from 2.72 to 7.69, and for IRI PLAS 2017 it varied from 7.39 to 11.24. Likewise, evaluation done for three different models over five different X-class solar flare events showed that the COKSM performed well during the high-intensity solar flare conditions. On the other hand, the FFNN model performed well during high-resolution input data conditions. Also, it is notable that both models performed better than the IRI PLAS 2017 model and are suitable for navigational applications.
{"title":"Ionospheric TEC prediction using FFNN during five different X Class solar flares of 2021 and 2022 and comparison with COKSM and IRI PLAS 2017","authors":"Sarat C. Dass, Raju Mukesh, Muthuvelan Vijay, Sivavadivel Kiruthiga, Shunmugam Mythili","doi":"10.1515/jag-2023-0057","DOIUrl":"https://doi.org/10.1515/jag-2023-0057","url":null,"abstract":"Abstract The Ionospheric Total Electron Content (TEC) measured in the ray path of the signals directly contributes to the Range Error (RE) of the satellite signals, which affects positioning and navigation. Employing the Co-Kriging-based Surrogate Model (COKSM) to predict TEC and RE correction has proven prolific. This research attempted to test and compare the prediction capability of COKSM with an Artificial Intelligence-based Feed Forward Neural Network model (FFNN) during five X-Class Solar Flares of 2021–22. Also, the results are validated by comparing them with the IRI PLAS 2017 model. TEC, solar, and geomagnetic parameters data for Hyderabad GPS station located at 17.31° N latitude and 78.55° E longitude were collected from IONOLAB & OMNIWEB servers. The COKSM uses six days of input data to predict the 7th day TEC, whereas prediction using the FFNN model is done using 45 days of data before the prediction date. The performance evaluation is done using RMSE, NRMSE, Correlation Coefficient, and sMAPE. The average RMSE for COKSM varied from 1.9 to 9.05, for FFNN it varied from 2.72 to 7.69, and for IRI PLAS 2017 it varied from 7.39 to 11.24. Likewise, evaluation done for three different models over five different X-class solar flare events showed that the COKSM performed well during the high-intensity solar flare conditions. On the other hand, the FFNN model performed well during high-resolution input data conditions. Also, it is notable that both models performed better than the IRI PLAS 2017 model and are suitable for navigational applications.","PeriodicalId":45494,"journal":{"name":"Journal of Applied Geodesy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134948261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract PPP_Mansoura is a new software that can process multi-GNSS data work on MATLAB environment and linked with C# in the preprocessing stage. It gives highly accurate results and provides a results file for each epoch, and the users can choose the GNSS system they want to run with the primary systems (GPS or GLONASS) and all this with simple MATLAB Code. For testing the software, we processed the raw data (RINEX 3) from 17 MGEX stations for 24 h data during 1-week with a 30-s interval time and submitted it to the new software and PPPH software. The averaged positioning errors obtained from PPP_Mansoura and PPPH were 5.14 mm and 6.9 mm respectively, for the East direction, 11.6 mm and 14 mm for the North direction, and 14.56 mm and 20.4 mm respectively for the Up direction, the averaged convergence time obtained from PPP_Mansoura and PPPH were 35.3 min and 54.47 min, so the results show that PPP_Mansoura give results with high accuracy can be comparable with PPP standards results and PPP software results.
{"title":"PPP_Mansoura: an open-source software for multi-constellation GNSS processing","authors":"Islam A. Kandil, Ahmed A. Awad, Mahmoud El-Mewafi","doi":"10.1515/jag-2023-0043","DOIUrl":"https://doi.org/10.1515/jag-2023-0043","url":null,"abstract":"Abstract PPP_Mansoura is a new software that can process multi-GNSS data work on MATLAB environment and linked with C# in the preprocessing stage. It gives highly accurate results and provides a results file for each epoch, and the users can choose the GNSS system they want to run with the primary systems (GPS or GLONASS) and all this with simple MATLAB Code. For testing the software, we processed the raw data (RINEX 3) from 17 MGEX stations for 24 h data during 1-week with a 30-s interval time and submitted it to the new software and PPPH software. The averaged positioning errors obtained from PPP_Mansoura and PPPH were 5.14 mm and 6.9 mm respectively, for the East direction, 11.6 mm and 14 mm for the North direction, and 14.56 mm and 20.4 mm respectively for the Up direction, the averaged convergence time obtained from PPP_Mansoura and PPPH were 35.3 min and 54.47 min, so the results show that PPP_Mansoura give results with high accuracy can be comparable with PPP standards results and PPP software results.","PeriodicalId":45494,"journal":{"name":"Journal of Applied Geodesy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134947254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The GPS Aided Geo Augmented Navigation (GAGAN) system provides the navigational services for single-frequency GNSS user via broadcasting the differential corrections with GEO stationary satellites. The significant differential correction contribution comes from ionospheric time delays and is necessary to be determined precisely. Dual-frequency GPS receivers measure the ionospheric time delays using GPS code and carrier phase measurements. The determination of absolute ionospheric Total Electron Content (TEC) requires the calibration of GPS satellites and receiver hardware biases due to different frequency-dependent signals (L1 and L2) due to environmental changes (Temperature and Humidity). In this paper, A receiver-based Differential Code Biases (DCB) algorithm is implemented to derive a joint estimation of TEC and RDCB parameters using the weighted Least Square (WLS) method. The daily averaged DCBs data for 26 GPS receivers are obtained for 3 years (2014–2016) from 26 GPS reeivers over Indian region. The receiver DCB algorithmis validated with the Fitted Receiver Biases (FRB) method. The correlation (R) between VTEC and RDCB is conducted to investigate the RDCB stability. The results would be useful for the accurate determination of ionospheric differential corrections to GAGAN users.
{"title":"Analysis of differential code biases for GPS receivers over the Indian region","authors":"Kondaveeti Sivakrishna, Devanaboyina Venkata Ratnam","doi":"10.1515/jag-2023-0047","DOIUrl":"https://doi.org/10.1515/jag-2023-0047","url":null,"abstract":"Abstract The GPS Aided Geo Augmented Navigation (GAGAN) system provides the navigational services for single-frequency GNSS user via broadcasting the differential corrections with GEO stationary satellites. The significant differential correction contribution comes from ionospheric time delays and is necessary to be determined precisely. Dual-frequency GPS receivers measure the ionospheric time delays using GPS code and carrier phase measurements. The determination of absolute ionospheric Total Electron Content (TEC) requires the calibration of GPS satellites and receiver hardware biases due to different frequency-dependent signals (L1 and L2) due to environmental changes (Temperature and Humidity). In this paper, A receiver-based Differential Code Biases (DCB) algorithm is implemented to derive a joint estimation of TEC and RDCB parameters using the weighted Least Square (WLS) method. The daily averaged DCBs data for 26 GPS receivers are obtained for 3 years (2014–2016) from 26 GPS reeivers over Indian region. The receiver DCB algorithmis validated with the Fitted Receiver Biases (FRB) method. The correlation (R) between VTEC and RDCB is conducted to investigate the RDCB stability. The results would be useful for the accurate determination of ionospheric differential corrections to GAGAN users.","PeriodicalId":45494,"journal":{"name":"Journal of Applied Geodesy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134947922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Laser scanning is a wide-spread practice to capture the environment. Besides the fields of robotics and self-driving cars, it has been applied in the field of engineering geodesy for documentation and monitoring purposes for many years. The registration of scans is still one of the main sources of uncertainty in the final point cloud. This paper presents a new keypoint-based method for terrestrial laser scan (TLS) registration for high-accuracy applications. Based on detected 2D-keypoints, we introduce a new statistical matching approach that tests wheter keypoints, scanned from two scan stations, can be assumed to be identical. This approach avoids the use of keypoint descriptors for matching and also handles wide distances between different scanner stations. The presented approach requires a good coarse registration as initial input, which can be achieved for example by artificial laser scanning targets. By means of two evaluation data sets, we show that our keypoint-based registration leads to the smallest loop closure error when traversing several stations compared to target-based and ICP registrations. Due to the high number of observations compared to the target-based registration, the reliability of the our keypoint-based registration can be increased significantly and the precision of the registration can be increased by about 25 % on average.
{"title":"Keypoint-based registration of TLS point clouds using a statistical matching approach","authors":"Jannik Janßen, Heiner Kuhlmann, Christoph Holst","doi":"10.1515/jag-2022-0058","DOIUrl":"https://doi.org/10.1515/jag-2022-0058","url":null,"abstract":"Abstract Laser scanning is a wide-spread practice to capture the environment. Besides the fields of robotics and self-driving cars, it has been applied in the field of engineering geodesy for documentation and monitoring purposes for many years. The registration of scans is still one of the main sources of uncertainty in the final point cloud. This paper presents a new keypoint-based method for terrestrial laser scan (TLS) registration for high-accuracy applications. Based on detected 2D-keypoints, we introduce a new statistical matching approach that tests wheter keypoints, scanned from two scan stations, can be assumed to be identical. This approach avoids the use of keypoint descriptors for matching and also handles wide distances between different scanner stations. The presented approach requires a good coarse registration as initial input, which can be achieved for example by artificial laser scanning targets. By means of two evaluation data sets, we show that our keypoint-based registration leads to the smallest loop closure error when traversing several stations compared to target-based and ICP registrations. Due to the high number of observations compared to the target-based registration, the reliability of the our keypoint-based registration can be increased significantly and the precision of the registration can be increased by about 25 % on average.","PeriodicalId":45494,"journal":{"name":"Journal of Applied Geodesy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135768501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-22DOI: 10.1515/jag-2023-frontmatter4
{"title":"Frontmatter","authors":"","doi":"10.1515/jag-2023-frontmatter4","DOIUrl":"https://doi.org/10.1515/jag-2023-frontmatter4","url":null,"abstract":"","PeriodicalId":45494,"journal":{"name":"Journal of Applied Geodesy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136011384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hadeer Al-Eshmawy, Mohamed A. Abdelfatah, Gamal S. El-Fiky
Abstract The atmospheric Precipitable water vapor (PWV) is a variable key for weather forecasting and climate change. It is a considerable component of the atmosphere, influencing numerous atmospheric processes, and having physical characteristics. It can be measured directly using radiosonde stations (RS), which are not always accessible and difficult to measure with acceptable spatial and time precision. This study uses the artificial neural network (ANN) application to propose a simple model based on RS data to estimate PWV from surface metrological data. Ten RS stations were used to develop the new model for eight and a half years. In addition, two and a half years of data were used to validate the developed model. The study period is based on the data accessible between 2010 and 2020. The new model needs to collect (vapor pressure, temperature, latitude, longitude, height, day of year, and relative humidity) as input parameters in ANN to predict the PWV. The ANN model validations were based on the root mean square (RMS), correlation coefficient (CC), and T-test. According to the results, the proposed ANN can accurately predict the PWV over Egypt. The results of the new ANN model and eight other empirical models (Saastamoinen, Askne and Nordius, Okulov et al., Maghrabi et al., Phokate., Falaiye et al. (A&B), Qian et al. and ERA 5) are compared in addition, the new PWV model can achieve the best performance with RMS of 0.21 mm. The new model can serve as a will be of practical utility with a high degree of precision in PWV estimation.
大气可降水量(PWV)是天气预报和气候变化的变量关键。它是大气的重要组成部分，影响许多大气过程，并具有物理特性。它可以直接使用无线电探空站(RS)进行测量，这些探空站并不总是可以到达并且难以以可接受的空间和时间精度进行测量。本研究利用人工神经网络(ANN)的应用，提出了一种基于遥感数据的地面测量数据估算PWV的简单模型。10个RS站用了8年半的时间来开发新模型。此外，两年半的数据被用来验证所开发的模型。研究期间基于2010年至2020年之间可获得的数据。新模型需要在人工神经网络中收集(蒸汽压、温度、纬度、经度、高度、年份和相对湿度)作为输入参数来预测PWV。基于均方根(RMS)、相关系数(CC)和t检验对人工神经网络模型进行验证。结果表明，所提出的人工神经网络能够准确预测埃及上空的PWV。新的人工神经网络模型和其他八个经验模型(Saastamoinen, Askne和Nordius, Okulov等人，Maghrabi等人，Phokate。与faraiye et al. (A&B)， Qian et al.和ERA 5进行了比较，并且新PWV模型的RMS为0.21 mm，可以达到最佳性能。该模型可作为一种实用的模型，在PWV估计中具有较高的精度。
{"title":"A proposed neural network model for obtaining precipitable water vapor","authors":"Hadeer Al-Eshmawy, Mohamed A. Abdelfatah, Gamal S. El-Fiky","doi":"10.1515/jag-2023-0035","DOIUrl":"https://doi.org/10.1515/jag-2023-0035","url":null,"abstract":"Abstract The atmospheric Precipitable water vapor (PWV) is a variable key for weather forecasting and climate change. It is a considerable component of the atmosphere, influencing numerous atmospheric processes, and having physical characteristics. It can be measured directly using radiosonde stations (RS), which are not always accessible and difficult to measure with acceptable spatial and time precision. This study uses the artificial neural network (ANN) application to propose a simple model based on RS data to estimate PWV from surface metrological data. Ten RS stations were used to develop the new model for eight and a half years. In addition, two and a half years of data were used to validate the developed model. The study period is based on the data accessible between 2010 and 2020. The new model needs to collect (vapor pressure, temperature, latitude, longitude, height, day of year, and relative humidity) as input parameters in ANN to predict the PWV. The ANN model validations were based on the root mean square (RMS), correlation coefficient (CC), and T-test. According to the results, the proposed ANN can accurately predict the PWV over Egypt. The results of the new ANN model and eight other empirical models (Saastamoinen, Askne and Nordius, Okulov et al., Maghrabi et al., Phokate., Falaiye et al. (A&B), Qian et al. and ERA 5) are compared in addition, the new PWV model can achieve the best performance with RMS of 0.21 mm. The new model can serve as a will be of practical utility with a high degree of precision in PWV estimation.","PeriodicalId":45494,"journal":{"name":"Journal of Applied Geodesy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134911308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Integrated water vapor (IWV) has been widely perceived through machine learning (ML) strategies. During this investigation, we employed IWV time series from weather stations to determine the oscillations and patterns with IWV across two latitudes namely VBIT, Hyderabad (India) and PWVUO station, Oregon (US). The GPS derived IWV and meteorological data such as pressure ( P ), temperature ( T ) and relative humidity (RH) dataset for the year 2014 has been taken from VBIT station and from PWVUO station for 2020. Five machine learning algorithms namely Optimized Ensemble (OE) model, Rational Quadratic Gaussian Process Regression model (RQ-GPR), Neural Networks model (NN), Cubic Support Vector Machine (CSVM) and Quadratic Support Vector Machine (QSVM) algorithms are used. The GPS derived IWV data revealed the maximum variation during summer monsoon period specifically in the month of July. The correlation analysis between GPS-IWV and optimized ensemble technique showed the highest correlation for the VBIT station with correlation coefficient as ( ρ ) = 99 % and at PWVUO station as ( ρ ) = 88 % for two different datasets. The residual analysis has also showed less variation to the optimized ensemble model. The performance metrics obtained for OE at VBIT station are mean absolute error (MAE) as 0.64 kg/m 2 , mean absolute percentage error (MAPE) as 3.80 % and root mean squared error (RMSE) as 0.94 kg/m 2 and at PWVUO station the values are MAE = 1.91 kg/m 2 , MAPE = 11.76 % and RMSE as 1.97 kg/m 2 , respectively. The results explained that the OE method has shown a better performance compared to the remaining models.
{"title":"Ensemble based deep learning model for prediction of integrated water vapor (IWV) using GPS and meteorological observations","authors":"Nirmala Bai Jadala, Miriyala Sridhar, Devanaboyina Venkata Ratnam, Surya Narayana Murthy Tummala","doi":"10.1515/jag-2023-0053","DOIUrl":"https://doi.org/10.1515/jag-2023-0053","url":null,"abstract":"Abstract Integrated water vapor (IWV) has been widely perceived through machine learning (ML) strategies. During this investigation, we employed IWV time series from weather stations to determine the oscillations and patterns with IWV across two latitudes namely VBIT, Hyderabad (India) and PWVUO station, Oregon (US). The GPS derived IWV and meteorological data such as pressure ( P ), temperature ( T ) and relative humidity (RH) dataset for the year 2014 has been taken from VBIT station and from PWVUO station for 2020. Five machine learning algorithms namely Optimized Ensemble (OE) model, Rational Quadratic Gaussian Process Regression model (RQ-GPR), Neural Networks model (NN), Cubic Support Vector Machine (CSVM) and Quadratic Support Vector Machine (QSVM) algorithms are used. The GPS derived IWV data revealed the maximum variation during summer monsoon period specifically in the month of July. The correlation analysis between GPS-IWV and optimized ensemble technique showed the highest correlation for the VBIT station with correlation coefficient as ( ρ ) = 99 % and at PWVUO station as ( ρ ) = 88 % for two different datasets. The residual analysis has also showed less variation to the optimized ensemble model. The performance metrics obtained for OE at VBIT station are mean absolute error (MAE) as 0.64 kg/m 2 , mean absolute percentage error (MAPE) as 3.80 % and root mean squared error (RMSE) as 0.94 kg/m 2 and at PWVUO station the values are MAE = 1.91 kg/m 2 , MAPE = 11.76 % and RMSE as 1.97 kg/m 2 , respectively. The results explained that the OE method has shown a better performance compared to the remaining models.","PeriodicalId":45494,"journal":{"name":"Journal of Applied Geodesy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135825620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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