Document Type
Article
Source of Publication
Journal of Big Data
Publication Date
12-1-2024
Abstract
This paper presents a comprehensive approach to harmonizing lunar calendars across different global regions, addressing the long-standing challenge of variations in new crescent Moon sightings that mark the beginning of lunar months. We propose a machine learning (ML)-based framework to predict the visibility of the new crescent Moon, representing a significant advancement toward a globally unified lunar calendar. Our study utilized a dataset covering various countries globally, making it the first to analyze all 12 lunar months over a span of 13 years. We applied a wide array of ML algorithms and techniques. These techniques included feature selection, hyperparameter tuning, ensemble learning, and region-based clustering, all aimed at maximizing the model’s performance. The overall results reveal that the gradient boosting (GB) model surpasses all other models, achieving the highest F1 score of 0.882469 and an area under the curve (AUC) of 0.901009. However, with selected features identified through the ANOVA F-test and optimized parameters, the Extra Trees model exhibited the best performance with an F1 score of 0.887872, and an AUC of 0.906242. We expanded our analysis to explore ensemble models, aiming to understand how a combination of models might boost predictive accuracy. The Ensemble Model exhibited a slight improvement, with an F1 score of 0.888058 and an AUC of 0.907482. Additionally, the geographical segmentation of the dataset enhanced predictive performance in certain areas, such as Africa and Asia. In conclusion, ML techniques can provide efficient and reliable tool for predicting the new crescent Moon visibility that would support the decisions of marking the beginning of new lunar months.
DOI Link
ISSN
Publisher
Springer Science and Business Media LLC
Volume
11
Issue
1
Disciplines
Computer Sciences
Keywords
Astronomy, Crescent visibility, Ensemble learning, Feature selection, Hyperparameter tuning, Lunar calendar, Machine learning, Prediction
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Loucif, Samia; Al-Rajab, Murad; Abu Zitar, Raed; and Rezk, Mahmoud, "Toward a globally lunar calendar: a machine learning-driven approach for crescent moon visibility prediction" (2024). All Works. 6827.
https://zuscholars.zu.ac.ae/works/6827
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series