AI-powered monitoring and forecasting of lunar months: enhancing accuracy and insights through machine learning - Lugga, Mohammed Sani; Nakazzale, Abdurrahman Umar (Direct Research Journal of Engineering and Information Technology, 2025)
The accurate determination of lunar months is crucial for various cultural, religious, and scientific practices. Traditional methods of observing the moon and calculating lunar months are time-consuming, prone to errors, and heavily reliant on human expertise. This study explores the potential of AI-powered monitoring and forecasting of lunar months, leveraging the capabilities of machine learning (ML) algorithms to enhance precision and provide deeper insights. By utilizing data from lunar observations, satellite imagery, and astronomical models, machine learning algorithms, including time series analysis, regression models, and deep learning techniques, are employed to predict the phases of the moon with increased accuracy. Furthermore, AI systems can identify subtle patterns in lunar cycles that are often overlooked by conventional methods, thus offering more reliable forecasts. This AI-driven approach not only promises to improve the accuracy of lunar month predictions but also enables real-time monitoring of lunar events, supporting decision-making in agriculture, religious observances, and space missions. The integration of AI into lunar forecasting represents a significant advancement in the field of astronomy, offering a scalable, efficient, and data-driven solution to lunar month predictions.