Comparison of Sequential, MLP, LSTM and Sequential Manned Machine Learning Methods for Producing Global Zenith Wet Delay

Published: 30 August 2022| Version 1 | DOI: 10.17632/7hcw5y2rs7.1
Contributor:
Jareer Mohammed

Description

Output for 8 methods: Sequential, MLP, LSTM and five Hybrid Scenarios (SQL-PSO-10, SQL-PSO-20, SQL-PSO-30, SQL-PSO-40, SQL-PSO-50). It contains validating and predicted data+ plot for each of the 505 Globally distributed stations.

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Steps to reproduce

These output based on the comparison of the machine learning methods for predicting the Zenith Wet Delay(ZWD) that is important for weather forecasting and GNSS meteorology. Output for 8 methods: Sequential, MLP, LSTM and five Hybrid Scenarios (SQL-PSO-10, SQL-PSO-20, SQL-PSO-30, SQL-PSO-40, SQL-PSO-50). It contains validating and predicted data+ plot for each of the 505 Globally distributed stations.

Categories

Weather Forecasting, Tropospheric Propagation Delays

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