IMPROVING SHORT-TERM WEATHER FORECASTING USING SUPPORT VECTOR MACHINE METHOD IN NORTH BARITO - Vista Wulandari, Ayu
Flooding almost occurs every year in Barito Utara Regency caused by the overflow of Barito River passing through the area. Weather information provided in Barito Utara is mostly still relying on outputs from Numerical Weather Prediction (NWP) models. However, NWP models represent weather conditions differently from the actual conditions by homogenizing the original surface conditions as grids, so the models are not always able to capture small local details with high variations. Therefore, statistical blending techniques are needed that can represent local conditions and improve NWP model outputs such as Model Output Statistics (MOS). This technique can be applied operationally without requiring highspecification equipment because it develops statistical relationships between the response variables (predictands) and predictor variables from NWP model outputs. In this study, rainfall data from 2021-2022 from the Beringin Meteorological Station in Barito Utara will be used as the response variable and data from the Integrating Forecasting System (IFS) model will be used as predictor variables. The Support Vector Machine (SVM) method is used to find the relationship between predictor and response variables. The results show improvements in RMSE, bias, and correlation values in the SVM model results against the original IFS model values. The percentage increase in improvement (%IM) by rainfall forecasts corrected using the SVM model also shows an increase of 5.03%.