Download Deep Learning Model For Predicting Daily IGS Zenith Tropospheric Delays in West Africa PDF Free - Full Version
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About Deep Learning Model For Predicting Daily IGS Zenith Tropospheric Delays in West Africa
This document presents a study that uses a deep learning model with TensorFlow and Keras to predict daily zenith tropospheric delays (ZTDs) over four Global Navigation Satellite System (GNSS) stations in West Africa. The model is trained on daily surface meteorological data and ZTD measurements from 2015-2018 and validated on 2019 data. Statistical analyses show the deep learning model (DLztd) can exceptionally well predict ZTDs, with average metrics of 3.25 mm for mean bias, 9.62 mm for root mean squared error, and 0.30% for mean absolute percentage error. This deep learning approach provides an alternative for improving the availability of ZTD data when measurements from the GNSS stations are
Detailed Information
| Author: | ['Samuel Osah'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 20 |
| Language: | English |
| Format: | |
| Price: | FREE |
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