Rainfall Forecasting in Central Lombok Using an Enhanced Facebook Prophet Model with Multiplicative Seasonality

Abstract

Accurate rainfall forecasting remains critical for climate-sensitive agricultural planning, particularly in monsoon-driven regions where rice production is highly vulnerable to hydrometeorological variability. In this study, rainfall in Central Lombok Regency was forecasted using an enhanced version of the Facebook Prophet model incorporating a multiplicative seasonal component. Univariate monthly rainfall data (measured in millimeters) from January 1991 to July 2024 were utilized to train and evaluate the model. A configuration yielding optimal performance produced a Mean Absolute Percentage Error (MAPE) of 18.08% on the testing set, with 80.19% of the forecasted values exhibiting an Absolute Percentage Error (APE) below 20%, indicating a high level of predictive reliability. Forecasting was conducted over a short-term horizon of nine 10-day periods (approximately three months). Analysis of the forecast outputs identified the transition period from the dry to the rainy season—early August to late October—as the most favorable window for initiating rice planting. By aligning planting schedules with anticipated rainfall patterns, the likelihood of crop failure can be mitigated, thereby enhancing productivity and supporting local food security. The findings underscore the practical utility of interpretable time series models in developing data-driven agricultural calendars and advancing climate-resilient farming practices. This approach is particularly relevant for tropical monsoon regions facing increasingly erratic rainfall due to climate change. Furthermore, the demonstrated integration of seasonality effects within the Prophet framework contributes methodologically to the broader field of agro-meteorological forecasting.