Autoregressive modelling of tropospheric radio refractivity over selected locations in tropical Nigeria using artificial neural network

Tropospheric radio refractivity is a significant atmospheric phenomenon that affects the propagation of radio signals, and can impact the design and operation of wireless communication systems. This study focuses on the development of an autoregressive model of tropospheric radio refractivity in Nigeria using artificial neural networks (ANNs). The proposed model utilizes atmospheric variables—temperature, pressure, and humidity—as inputs and predicts refractivity values with high accuracy. Descriptive statistics and data visualization techniques were used to gain insights into the relationships between the atmospheric variables and computed radio refractivity. It could be deduced from the results obtained that the developed ANN model accurately predicts tropospheric radio refractivity, with satisfactory performance indicators that include standard error (SE), root mean square error (RMSE), and correlation coefficient (R). It also demonstrates the reliability and robustness of the developed model, which could play an important role in improving the preparation and implementation routines of wireless communication systems. The study also identifies areas for further study, such as data availability, model complexity, and interpretability. Lastly, this work has further validated the suitability of applying ANNs to tropospheric radio refractivity model optimization, as it provides insights into the potential of the non-linear autoregressive modeling (NARX-ANN) approach for improving wireless communication systems.