Research Article
Two-Stage Statistical Downscaling Modeling with Multi-Class Random Forest on Rainfall Prediction
@INPROCEEDINGS{10.4108/eai.2-8-2019.2290525, author={Riana Hadiana and Agus Mohamad Soleh and Bagus Sartono}, title={Two-Stage Statistical Downscaling Modeling with Multi-Class Random Forest on Rainfall Prediction}, proceedings={Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia}, publisher={EAI}, proceedings_a={ICSA}, year={2020}, month={1}, keywords={multi-class partial least square regression random forest statistical downscaling}, doi={10.4108/eai.2-8-2019.2290525} }- Riana Hadiana
Agus Mohamad Soleh
Bagus Sartono
Year: 2020
Two-Stage Statistical Downscaling Modeling with Multi-Class Random Forest on Rainfall Prediction
ICSA
EAI
DOI: 10.4108/eai.2-8-2019.2290525
Abstract
Statistical downscaling (SD) modeling to predict rainfall has been widely used using the General Circulation Model (GCM) output. Based on the previous study, SD modeling to predict rainfall by rainfall grouping (two-stages) gives a smaller Root Mean Squares Error of Prediction (RMSEP) than SD modeling without rainfall grouping (one-stage). In this study, the daily and monthly rainfall were divided into three groups based on their intensity (volume) and two-stages SD modeling was applied to predict rainfall. The first stage was rainfall groups classification using random forest. The second stage was rainfall prediction using Partial Least Squares Regression (PLSR). The accuracy obtained by random forest for daily and monthly rainfall lied between 62%-84%. The RMSEP obtained from two-stages SD modeling for daily rainfall was similar to one-stage SD modeling, where the Coefficient of Variation (CV) was above 100%. The different results happened when two-stages SD modeling was applied to monthly data. The RMSEP obtained was better than one-stage SD modeling, where the CV lied between 30%-50%.