When Astronomy Meets AI: Manazel For Crescent Visibility Prediction in Morocco
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912297493987328 |
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| author | Lairgi, Yassir |
| author_facet | Lairgi, Yassir |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21634 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | When Astronomy Meets AI: Manazel For Crescent Visibility Prediction in Morocco Lairgi, Yassir Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | When Astronomy Meets AI: Manazel For Crescent Visibility Prediction in Morocco |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.21634 |