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Improving the Forecasting Accuracy Based on the Lunar Calendar in Modeling Rainfall Levels Using the Bi-LSTM Method

Gumgum Darmawan¹, Budhi Handoko¹, Defi Yusti Faidah¹

¹Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Jl.Bandung-Sumedang Km 21 Jatinangor, Sumedang 45363, Indonesia.

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Summary

This summary is machine-generated.

This study compared rainfall forecasting accuracy using Gregorian and lunar calendars. The lunar calendar demonstrated superior forecasting ability, with a smaller Mean Absolute Percentage Error (MAPE) than the Gregorian calendar.

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Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface

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Area of Science:

Meteorology and Climatology
Data Science and Machine Learning

Background:

Rainfall forecasting is crucial for human activities and disaster prevention.
Climatic factors like rainfall significantly impact daily life and decision-making.
High-intensity rainfall can lead to natural disasters, necessitating accurate prediction.

Purpose of the Study:

To compare the accuracy of rainfall forecasting between the Gregorian and lunar calendars.
To evaluate the effectiveness of the bidirectional long short-term memory (Bi-LSTM) model in capturing patterns from asynchronous calendars.
To determine which calendar system provides more accurate monthly rainfall predictions.

Main Methods:

Utilized monthly rainfall data from Bogor City, Indonesia (2001-2022).
Employed the bidirectional long short-term memory (Bi-LSTM) machine learning model.
Applied a grid search approach to optimize model parameters for both calendar systems.

Main Results:

The lunar calendar achieved a Mean Absolute Percentage Error (MAPE) of 14.82%.
The Gregorian calendar resulted in a higher MAPE of 35.12%.
The Bi-LSTM model, with grid search optimization, showed better performance with the lunar calendar.

Conclusions:

The lunar calendar offers significantly better rainfall forecasting accuracy compared to the Gregorian calendar.
The Bi-LSTM model effectively captures temporal patterns for rainfall prediction across different calendar systems.
Accurate rainfall forecasting using lunar calendar data can aid in better planning for human activities and disaster mitigation.