Radio broadcasting has become integral to human society, providing information, entertainment, and fast communications. However, the quality of radio signal reception can be immensely affected by various factors, including weather conditions. This study investigates how climatic conditions, specifically changes in temperature and wind speed, affect radio signal transmission for Pride FM in Gusau, Zamfara State, Nigeria. Signal strength and quality can be significantly influenced by environmental conditions, making it essential to understand the impact of temperature and wind speed variations to ensure reliable broadcasting operations in Gusau City. Meteorological data were obtained from credible sources, along with signal transmission data, to facilitate this analysis. The logs were preprocessed. A machine learning regression model was developed to predict signal variations based on prevailing weather conditions. A robust data preprocessing approach was employed, including data cleaning and imputing missing values with their mean to enhance accuracy and reliability of the model. Regression analysis revealed an inverse relationship between temperature, wind speed, and signal strength. The results indicate that higher temperatures and wind speeds contribute to signal degradation, whereas lower values enhance signal strength. The resulting prediction model could serve as a valuable tool for improving radio signal transmission quality, enabling engineers to anticipate losses and optimize transmission parameters for consistent broadcasting operations.
