Lunar Calendar Usage to Improve Forecasting Accuracy Rainfall via Machine Learning Methods
The lunar calendar is often overlooked in time-series data modeling despite its importance in understanding seasonal patterns, as well as economics, natural phenomena, and consumer behavior. This study aimed to investigate the effectiveness of the lunar calendar in modeling and forecasting rainfall...
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/675 |
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| author | Gumgum Darmawan Gatot Riwi Setyanto Defi Yusti Faidah Budhi Handoko |
| author_facet | Gumgum Darmawan Gatot Riwi Setyanto Defi Yusti Faidah Budhi Handoko |
| author_sort | Gumgum Darmawan |
| collection | DOAJ |
| description | The lunar calendar is often overlooked in time-series data modeling despite its importance in understanding seasonal patterns, as well as economics, natural phenomena, and consumer behavior. This study aimed to investigate the effectiveness of the lunar calendar in modeling and forecasting rainfall levels using various machine learning methods. The methods employed included long short-term memory (LSTM) and gated recurrent unit (GRU) models to test the accuracy of rainfall forecasts based on the lunar calendar compared to those based on the Gregorian calendar. The results indicated that machine learning models incorporating the lunar calendar generally provided greater accuracy in forecasting for periods of 3, 4, 6, and 12 months compared to models using the Gregorian calendar. The lunar calendar model demonstrated higher accuracy in its prediction, exhibiting smaller errors (MAPE and MBE values), whereas the Gregorian calendar model yielded somewhat larger errors and tended to underestimate the values. These findings contributed to the advancement of forecasting techniques, machine learning, and the adaptation to non-Gregorian calendar systems while also opening new opportunities for further research into lunar calendar applications across various domains. |
| format | Article |
| id | doaj-art-197cb7261da543da90d6230bc9cf068c |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-197cb7261da543da90d6230bc9cf068c2025-01-24T13:20:24ZengMDPI AGApplied Sciences2076-34172025-01-0115267510.3390/app15020675Lunar Calendar Usage to Improve Forecasting Accuracy Rainfall via Machine Learning MethodsGumgum Darmawan0Gatot Riwi Setyanto1Defi Yusti Faidah2Budhi Handoko3Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Bandung 45363, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Bandung 45363, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Bandung 45363, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Bandung 45363, IndonesiaThe lunar calendar is often overlooked in time-series data modeling despite its importance in understanding seasonal patterns, as well as economics, natural phenomena, and consumer behavior. This study aimed to investigate the effectiveness of the lunar calendar in modeling and forecasting rainfall levels using various machine learning methods. The methods employed included long short-term memory (LSTM) and gated recurrent unit (GRU) models to test the accuracy of rainfall forecasts based on the lunar calendar compared to those based on the Gregorian calendar. The results indicated that machine learning models incorporating the lunar calendar generally provided greater accuracy in forecasting for periods of 3, 4, 6, and 12 months compared to models using the Gregorian calendar. The lunar calendar model demonstrated higher accuracy in its prediction, exhibiting smaller errors (MAPE and MBE values), whereas the Gregorian calendar model yielded somewhat larger errors and tended to underestimate the values. These findings contributed to the advancement of forecasting techniques, machine learning, and the adaptation to non-Gregorian calendar systems while also opening new opportunities for further research into lunar calendar applications across various domains.https://www.mdpi.com/2076-3417/15/2/675GRUforecastingLSTMlunar calendarmachine learningrainfall |
| spellingShingle | Gumgum Darmawan Gatot Riwi Setyanto Defi Yusti Faidah Budhi Handoko Lunar Calendar Usage to Improve Forecasting Accuracy Rainfall via Machine Learning Methods Applied Sciences GRU forecasting LSTM lunar calendar machine learning rainfall |
| title | Lunar Calendar Usage to Improve Forecasting Accuracy Rainfall via Machine Learning Methods |
| title_full | Lunar Calendar Usage to Improve Forecasting Accuracy Rainfall via Machine Learning Methods |
| title_fullStr | Lunar Calendar Usage to Improve Forecasting Accuracy Rainfall via Machine Learning Methods |
| title_full_unstemmed | Lunar Calendar Usage to Improve Forecasting Accuracy Rainfall via Machine Learning Methods |
| title_short | Lunar Calendar Usage to Improve Forecasting Accuracy Rainfall via Machine Learning Methods |
| title_sort | lunar calendar usage to improve forecasting accuracy rainfall via machine learning methods |
| topic | GRU forecasting LSTM lunar calendar machine learning rainfall |
| url | https://www.mdpi.com/2076-3417/15/2/675 |
| work_keys_str_mv | AT gumgumdarmawan lunarcalendarusagetoimproveforecastingaccuracyrainfallviamachinelearningmethods AT gatotriwisetyanto lunarcalendarusagetoimproveforecastingaccuracyrainfallviamachinelearningmethods AT defiyustifaidah lunarcalendarusagetoimproveforecastingaccuracyrainfallviamachinelearningmethods AT budhihandoko lunarcalendarusagetoimproveforecastingaccuracyrainfallviamachinelearningmethods |