Open AccessPrincipal Component Regression in Statistical Downscaling with Missing Value for Daily Rainfall Forecasting
Author(s) -
M Dika Saputra,
Alfian Futuhul Hadi,
Abduh Riski,
Dian Anggraeni
Publication year - 2021
Publication title -
international journal of quantitative research and modeling
Language(s) - English
Resource type - Journals
eISSN - 2722-5046
pISSN - 2721-477X
DOI - 10.46336/ijqrm.v2i3.151
Subject(s) - downscaling , principal component analysis , mean squared error , missing data , autoregressive integrated moving average , kalman filter , statistics , mean absolute percentage error , principal component regression , climatology , imputation (statistics) , mathematics , meteorology , environmental science , econometrics , geography , precipitation , time series , geology
Drought is a serious problem that often arises during the dry season. Hydrometeorologically, drought is caused by reduced rainfall in a certain period. Therefore, it is necessary to take the latest actions that can overcome this problem. This research aims to predict the potential for a drought to occur again in the Kupang City, Indonesia by developing a rainfall forecasting model. Incomplete daily local climate data for Kupang City is an obstacle in this analysis of rainfall forecasting. Data correction was then carried out through imputed missing values using the Kalman Filter method with Arima State-Space model. The Kalman Filter and Arima State-Space model (2,1,1) produces the best missing data imputation with a Root Mean Square Error (RMSE) of 0.930. The rainfall forecasting process is carried out using Statistical Downscaling with the Principal Component Regression (PCR) model that considers global atmospheric circulation from the Global Circular Model (GCM). The results showed that the PCR model obtained was quite good with a Mean Absolute Percent Error (MAPE) value of 2.81%. This model is used to predict the daily rainfall of Kupang City by utilizing GCM data.