A Combined Approach to Precipitation Forecasting: Enhancing FB–Prophet With Fuzzy Clustering to Capture Sudden Changes and Seasonal Patterns in Climate Data

Abstract

ABSTRACT Accurate precipitation prediction is vital for effective water resource management, agricultural planning, and natural disaster mitigation. Traditional forecasting methods often encounter difficulties due to the nonlinearity, complex seasonality, and noise inherent in meteorological data. This paper introduces a novel methodology that combines the FB–Prophet algorithm, designed by Facebook for identifying trends and seasonal patterns, with a fuzzy clustering algorithm. This integration aims to refine a crucial aspect of the FB–Prophet framework: the identification and incorporation of special events, specifically holidays, which play a significant role in the predictive modeling process. This approach ensures that holidays are effectively integrated into forecasts, enhancing the model's overall accuracy and reliability. Additionally, the proposed model is compared to several widely used algorithms in recent studies in terms of accuracy, employing nonparametric tests for a robust evaluation. Empirical results demonstrate a significant improvement in forecast accuracy over traditional methods.