Abstract In recent years, machine learning techniques, especially artificial neural networks (ANN), have been widely applied for engineering problems since they have proven to be a good predictor, especially in accurate weather forecasting applications. The study aims to evaluate the prediction capability of the novel hybrid ANN-GTO (Gorilla Troops Optimizer), which is not applied for predicting the zenith wet delay (ZWD) in the earlier literature, and the developed ANN-GTO has been compared with two training algorithms, namely Levenberg–Marquardt and Bayesian Regularization, the performance of the ANN-GTO has also been compared with improved ANN methods to illustrate the efficiency of ANN-GTO. The results of all the developed models have been compared via performance criteria. Three inputs, such as pressure, temperature, and water vapour pressure (WVP) have been employed for model training and testing purposes. The analysis outcomes have disclosed that all the ANN enhanced with GTO have overachieved the classic ANN and other hybrid ANN models in predicting ZWD.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Tropospheric zenith wet delay prediction with a new hybrid ANN – Gorilla troops optimizer algorithm


    Contributors:

    Published in:

    Advances in Space Research ; 71 , 11 ; 4739-4750


    Publication date :

    2023-01-13


    Size :

    12 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English