Forecasting tropospheric wet delay using LSTM neural network

Tropospheric wet delay is a critical factor in radio communication. Accurate estimation of the wet delay is difficult due to the variability in water vapour. In this study, we aim to model and predict tropospheric wet delay over four tropical locations using Long Short-Term Memory (LSTM) neural netw...

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Published in:IOP conference series. Earth and environmental science 2022-03, Vol.993 (1), p.12024
Main Authors: Ogunjo, S.T., Dada, J.B., Ajayi, O.J.
Format: Article
Language:English
Subjects:
Communication
Delay
Long short-term memory
LSTM
Neural network
Neural networks
Radio
Radio communications
Tropical environment
Tropical environments
Troposphere
Water vapor
Wet delay
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description Tropospheric wet delay is a critical factor in radio communication. Accurate estimation of the wet delay is difficult due to the variability in water vapour. In this study, we aim to model and predict tropospheric wet delay over four tropical locations using Long Short-Term Memory (LSTM) neural network. Results obtained in this study showed RMSE and MAE within the range 18.96–21.16 and 14.08–16.38 respectively. LSTM model was able to capture the different regimes of wet delays in each of the locations under consideration. This approach can significantly improve link budget and planning within tropical regions.
doi_str_mv 10.1088/1755-1315/993/1/012024
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subjects Communication
Delay
Long short-term memory
LSTM
Neural network
Neural networks
Radio
Radio communications
Tropical environment
Tropical environments
Troposphere
Water vapor
Wet delay
title Forecasting tropospheric wet delay using LSTM neural network
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