STACKING ENSEMBLE APPROACH IN STATISTICAL DOWNSCALING USING CMIP6-DCPP FOR RAINFALL ESTIMATION IN RIAU - Mahkya, Dani Al; Djuraidah, Anik; Wigena, Aji Hamim; Sartono, Bagus (Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro, 2024)
Rainfall modeling and prediction is one of the important things to do. Rainfall has an important relationship and role with various aspects of the environment. One phenomenon that can be associated with rainfall is forest and land fires. Riau is one of the provinces in Indonesia that has a high potential for forest and land fires. This is because Riau has a large area of peatland. One approach that can be used to estimate rainfall is statistical downscaling. The concept of this approach is to form a functional relationship between global and local data. This research uses CMIP6-DCPP output data that will be used to estimate rainfall at 10 observation stations in Riau. The proposed model in this research is Stacking Ensemble with PC Regression and LASSO Regression in the base model and Multiple Linear Regression in the meta model. This research aims to determine the best CMIP6-DCPP model for estimating rainfall in Riau and increasing the accuracy of rainfall estimates using the Stacking Ensemble approach.