Using GNSS Observations for Tropospheric Delay Prediction Using Artificial Intelligence
GNSS technology holds significant importance across wide applications, ranging from mapping, surveying, and precise timekeeping to ship navigation. Its operational principle hinges on the accurate measurement of signal travel time, which is crucial for determining the distance between the GNSS satel...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Port Said University
2023-12-01
|
| Series: | Port Said Engineering Research Journal |
| Subjects: | |
| Online Access: | https://pserj.journals.ekb.eg/article_323834_05d20538048e80a606adda7f20486cb9.pdf |
| _version_ | 1827346695670726656 |
|---|---|
| author | Ahmed Sedeek |
| author_facet | Ahmed Sedeek |
| author_sort | Ahmed Sedeek |
| collection | DOAJ |
| description | GNSS technology holds significant importance across wide applications, ranging from mapping, surveying, and precise timekeeping to ship navigation. Its operational principle hinges on the accurate measurement of signal travel time, which is crucial for determining the distance between the GNSS satellite and the receiving device. However, the precision of GNSS positioning is often compromised due to various error sources that impact GNSS measurements. Among these sources, atmospheric effects are widely acknowledged as the primary contributors to spatially correlated inaccuracies in GNSS (Global Navigation Satellite System) measurements. The accuracy of zenith tropospheric delay (ZTD) and zenith wet delay (ZWD) prediction using an artificial neural network model was successfully demonstrated in this study. By combining data from GNSS observations and in-situ meteorological measurements, high-resolution water vapour data can be produced for reliable and accurate weather forecasting. The validation of the predictions revealed a mean standard deviation error of 5 mm and 3.6 mm for ZTD and ZWD, respectively. This study emphasizes the significance of estimating tropospheric wet delay in real-time weather forecasting applications. |
| first_indexed | 2024-03-07T23:34:26Z |
| format | Article |
| id | doaj.art-579b361571094b28af3ebd72ab3b7385 |
| institution | Directory Open Access Journal |
| issn | 1110-6603 2536-9377 |
| language | English |
| last_indexed | 2024-03-07T23:34:26Z |
| publishDate | 2023-12-01 |
| publisher | Port Said University |
| record_format | Article |
| series | Port Said Engineering Research Journal |
| spelling | doaj.art-579b361571094b28af3ebd72ab3b73852024-02-20T07:52:06ZengPort Said UniversityPort Said Engineering Research Journal1110-66032536-93772023-12-01274343910.21608/pserj.2023.242830.1270323834Using GNSS Observations for Tropospheric Delay Prediction Using Artificial IntelligenceAhmed Sedeek015GNSS technology holds significant importance across wide applications, ranging from mapping, surveying, and precise timekeeping to ship navigation. Its operational principle hinges on the accurate measurement of signal travel time, which is crucial for determining the distance between the GNSS satellite and the receiving device. However, the precision of GNSS positioning is often compromised due to various error sources that impact GNSS measurements. Among these sources, atmospheric effects are widely acknowledged as the primary contributors to spatially correlated inaccuracies in GNSS (Global Navigation Satellite System) measurements. The accuracy of zenith tropospheric delay (ZTD) and zenith wet delay (ZWD) prediction using an artificial neural network model was successfully demonstrated in this study. By combining data from GNSS observations and in-situ meteorological measurements, high-resolution water vapour data can be produced for reliable and accurate weather forecasting. The validation of the predictions revealed a mean standard deviation error of 5 mm and 3.6 mm for ZTD and ZWD, respectively. This study emphasizes the significance of estimating tropospheric wet delay in real-time weather forecasting applications.https://pserj.journals.ekb.eg/article_323834_05d20538048e80a606adda7f20486cb9.pdfatmospheregnssprecise point positioningtroposphere |
| spellingShingle | Ahmed Sedeek Using GNSS Observations for Tropospheric Delay Prediction Using Artificial Intelligence Port Said Engineering Research Journal atmosphere gnss precise point positioning troposphere |
| title | Using GNSS Observations for Tropospheric Delay Prediction Using Artificial Intelligence |
| title_full | Using GNSS Observations for Tropospheric Delay Prediction Using Artificial Intelligence |
| title_fullStr | Using GNSS Observations for Tropospheric Delay Prediction Using Artificial Intelligence |
| title_full_unstemmed | Using GNSS Observations for Tropospheric Delay Prediction Using Artificial Intelligence |
| title_short | Using GNSS Observations for Tropospheric Delay Prediction Using Artificial Intelligence |
| title_sort | using gnss observations for tropospheric delay prediction using artificial intelligence |
| topic | atmosphere gnss precise point positioning troposphere |
| url | https://pserj.journals.ekb.eg/article_323834_05d20538048e80a606adda7f20486cb9.pdf |
| work_keys_str_mv | AT ahmedsedeek usinggnssobservationsfortroposphericdelaypredictionusingartificialintelligence |