Ten daily rainfall forecasting using SSA algorithms and Seasonal ARIMA model to determine the beginning of the rainy season

Abstract

Abstract Rainfall is one of the most important hydrological parameters. Based on the prediction of rainfall, several plans related to rainfall can be carried out, including predicting the start of the planting season based on information on the beginning of the rainy season and preparing cropping patterns. The purpose of this study is to predict the ten daily rainfall for 12 months from January to December of the following year and predict the start of the rainy season based on BMKG criteria. Modeling and forecasting is carried out on the ten daily regional rainfall in the catchment of Citarum-Majalaya water gauge using the Singular Spectrum Analysis (SSA) algorithm and the Seasonal ARIMA model. Forecasting model performance is measured using error values, namely mean absolute percentage error (MAPE). The results of these performance tests illustrate that the SSA algorithm and the Seasonal ARIMA model have fairly good accuracy for predicting rainfall for the next 6 months, where each has a MAPE value of 36.8 percent and 40.0 percent. Prediction of the beginning of the rainy season at the study location based on SSA forecasting results is most likely to occur on the 3rd of October, in accordance with BMKG predictions. whereas based on the results of the SARIMA forecasting model, the beginning of the rainy season is likely to occur on the 1st of November, and another possibility that the beginning of the rainy season will occur on the 2nd of October.