Faculty of Basic and Applied Sciences

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Start date: 2020Subject: search.filters.subject.Troposhere

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    Modelling Of Pressure-Altitude Equation Covering Up To 2km Above The Sea Level In Lagos, Southwestern Nigeria
    (IOSR, 2025-05-27) Yusuf Babatunde Lawal and Solomon Ziakede Koroye
    It is generally known that the atmospheric pressure varies inversely as altitude globally. In the absence of real time data, prediction of atmospheric pressure in the troposphere is based by the famous barometric equation which depicts exponential inverse relationship between these two variables. This work employed two years meteorological data of Lagos to study the relationship and model a localized equation. The research results show that the atmosphere is purely linear with a gradient of -10.1 m/hPa from the sea level to about 2 km. The modeled equation is useful for better prediction of atmospheric pressure for industrial, scientific and other purposes.
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    Machine learning-based models for forecasting radio refractivity over the coastal area of South Africa
    (Learning Gate, 2025-02-04) Yusuf Babatunde Lawal1*, Pius Adewale Owolawi2, Chunling Tu3, Etienne Van Wyk4, Joseph Sunday Ojo5
    Surface refractivity is a crucial parameter that determines the bending of radio signals as they propagate within the troposphere. It is greatly influenced by the atmospheric weather conditions and changes rapidly, especially in the coastal areas. This research utilized 50 years (1974-2023) surface temperature, pressure, and humidity data from six coastal stations in South Africa to forecast radio refractivity in the Mediterranean climate. Five machine learning models: Gated Recurrent Unit (GRU), Light Gradient Boosting Machine (LightGBM), Long-Short Term Memory (LSTM), Prophet, and Random Forest were trained for future prediction of surface refractivity at any coastal area in South Africa. The stations latitude, longitude, altitude, surface refractivity and date were applied as the input parameters to train the models. The models were optimized through the randomized searchCV hyperparameter tuning to improve their efficiency. The LightGBM outperformed other models with RMSE and adjusted determination coefficients of 1.67 and 0.96, respectively. The model is recommended for future prediction of surface refractivity needed for the improvement of point-to-point wireless communication, terrestrial radio and television transmissions, and mobile communication networks in the coastal sub-tropical regions.