Improving the Forecasting Accuracy Based on the Lunar Calendar in Modeling Rainfall Levels Using the Bi-LSTM Method through the Grid Search Approach.

Autor: Darmawan G; Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Jl.Bandung-Sumedang Km 21 Jatinangor, Sumedang 45363, Indonesia., Handoko B; Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Jl.Bandung-Sumedang Km 21 Jatinangor, Sumedang 45363, Indonesia., Faidah DY; Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Jl.Bandung-Sumedang Km 21 Jatinangor, Sumedang 45363, Indonesia., Islamiaty D; Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Jl.Bandung-Sumedang Km 21 Jatinangor, Sumedang 45363, Indonesia.
Jazyk: angličtina
Zdroj: TheScientificWorldJournal [ScientificWorldJournal] 2023 Dec 31; Vol. 2023, pp. 1863346. Date of Electronic Publication: 2023 Dec 31 (Print Publication: 2023).
DOI: 10.1155/2023/1863346
Abstrakt: Rainfall is one of the climatic factors that influence various human activities and affect decision making in daily life activities. High intensity of rainfall can turn into a threat and cause serious problems such as causing various natural disasters. Therefore, it is essential to conduct rainfall forecasting to anticipate and enable preventive actions and can be used as a decision consideration in increasing the productivity and mobility of human activities. The aim of this study is to compare rainfall accuracy between the Gregorian and the lunar calendars using the bidirectional long short-term memory (Bi-LSTM) machine learning model through the grid search approach. This method was used because it can capture patterns arising from the simultaneous effects of two asynchronous calendars, Gregorian and lunar, which were used in this study by finding the right parameters. Monthly rainfall data from Bogor City, Indonesia, were used from the period of 2001 to 2022. The results show that the MAPE of the lunar calendar is relatively smaller at 14.82% which indicates the better forecasting ability than the Gregorian calendar which is 35.12%.
Competing Interests: The authors declare that there are no conflicts of interest.
(Copyright © 2023 Gumgum Darmawan et al.)
Databáze: MEDLINE
Externí odkaz: