Statistical Downscaling with Bayesian Quantile Regression to Estimate Extreme Rainfall in West Java
AbstractWest Java is one of the largest regions producing rice in Indonesia. Information on rainfall is very important for farmers to anticipate extreme events that can cause losses in agriculture. Extreme rainfall patterns can be modeled by Bayesian quantile regression. Parameters of the model are estimated by MCMC. This study used statistical downscaling to obtain relationship between global scale data and local scale data. The data were monthly rainfall data in West Java based on three type of land, low, medium, high land, and GCM output data. LASSO regularization was used to overcome multicollinearity problem in GCM output data. The purpose of this study was to model Bayesian quantile regression in each type of land. The Bayesian quantile regression model in the lowlands can predict extreme rainfall accurately and consistently in one year ahead.
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