用13年数据优化月相预测，准确率达98.83%
When Astronomy Meets AI: Manazel For Crescent Visibility Prediction in Morocco
- 结合弧高与月牙宽度，用逻辑回归模型判断可见性
- 在13年数据上实现98.83%的预测准确率
- 为摩洛哥穆斯林历法提供可靠科学支持
准确确定每个伊斯兰月的开始对宗教、文化和行政具有重要意义。本文针对摩洛哥的这一需求，利用13年月牙可见性数据，改进了广泛使用的ODEH判据。研究引入弧高（ARCV）和月牙总宽度（W）两个关键特征，采用逻辑回归机器学习方法进行可见性分类，预测准确率达到98.83%。该数据驱动方法为确定伊斯兰月起始日提供了稳健可靠的框架，可用于比较不同分类工具并提升摩洛哥月历计算的一致性。结果表明机器学习在天文应用中的有效性，并展示了月牙可见性建模的进一步优化潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。