When Astronomy Meets AI: Manazel For Crescent Visibility Prediction in Morocco
This work addresses a specific religious and cultural need in Morocco for accurate lunar calendar calculations, representing an incremental improvement in a domain-specific application.
The study tackled the problem of predicting lunar crescent visibility to determine the start of the Hijri month in Morocco by refining the ODEH criterion using 13 years of data and machine learning, achieving a predictive accuracy of 98.83%.
The accurate determination of the beginning of each Hijri month is essential for religious, cultural, and administrative purposes. Manazel (The code and datasets are available at https://github.com/lairgiyassir/manazel) addresses this challenge in Morocco by leveraging 13 years of crescent visibility data to refine the ODEH criterion, a widely used standard for lunar crescent visibility prediction. The study integrates two key features, the Arc of Vision (ARCV) and the total width of the crescent (W), to enhance the accuracy of lunar visibility assessments. A machine learning approach utilizing the Logistic Regression algorithm is employed to classify crescent visibility conditions, achieving a predictive accuracy of 98.83%. This data-driven methodology offers a robust and reliable framework for determining the start of the Hijri month, comparing different data classification tools, and improving the consistency of lunar calendar calculations in Morocco. The findings demonstrate the effectiveness of machine learning in astronomical applications and highlight the potential for further enhancements in the modeling of crescent visibility.