Aji Hamim Wigena

Statistical Downscaling with Generalized Additive Model For Extreme Rainfall Estimation

The analysis of the phenomenon of extreme values of climate, especially rainfall is very important for government to reduce the negative impacts. Global circulation model (GCM) is an important data in the climate system because it can provide information about the climate in the future on a large scale. Techniques to reduce the size of the spatial scale using statistical downscaling (SD). SD modeling method requires a more flexible alternative to the assumption that the resulting models can be used to describe the climate events. Generalized additive model (GAM) is a method that accommodates the influence of linear and nonlinear in extreme rainfall events. The methodology is applied to forecast montly extreme rainfall in Indramayu District.

Mixture designs for quadratic models with constrained experimental regions on ground granulated blast furnace slag concrete

The nature of the ground granulated blast furnace slag (GGBFS) experiment followed a mixture design in which the response can be described by a quadratic model. The five mixture components were water, cement, coarse aggregate, fine aggregate, and percentage GGBFS replaced the cement). Due to the constraints of the components, the experimental region was not a simplex. The constraints of the mixture component affect the experimental region. For the irregular shape, the design points are difficult to identify by hand. The XVERT algorithm can be used for selecting a subset of extreme vertices and centroids when the number of candidates is large. The Fedorov algorithm used for selecting a design of available 1252 Faula Arina et al. vertices and centroids. Through the optimal design of the GGBFS experiment, experimental runs can be reduced more efficiently.

Vector generalized additive models for extreme rainfall data analysis (study case rainfall data in Indramayu)

Rainfall pattern are good indicators for potential disasters. Global Circulation Model (GCM) contains global scale information that can be used to predict the rainfall data. Statistical downscaling (SD) utilizes the global scale information to make inferences in the local scale. Essentially, SD can be used to predict local scale variables based on global scale variables. SD requires a method to accommodate non linear effects and extreme values. Extreme value Theory (EVT) can be used to analyze the extreme value. One of methods to identify the extreme events is peak over threshold that follows Generalized Pareto Distribution (GPD). The vector generalized additive model (VGAM) is an extension of the generalized additive model. It is able to accommodate linear or nonlinear effects by involving more than one additive predictors. The advantage of VGAM is to handle multi response models. The key idea of VGAM are iteratively reweighted least square for maximum likelihood estimation, penalized smoothing, fisher s...

Statistical downscaling modeling with quantile regression using lasso to estimate extreme rainfall

Rainfall is one of the climatic elements with high diversity and has many negative impacts especially extreme rainfall. Therefore, there are several methods that required to minimize the damage that may occur. So far, Global circulation models (GCM) are the best method to forecast global climate changes include extreme rainfall. Statistical downscaling (SD) is a technique to develop the relationship between GCM output as a global-scale independent variables and rainfall as a local- scale response variable. Using GCM method will have many difficulties when assessed against observations because GCM has high dimension and multicollinearity between the variables. The common method that used to handle this problem is principal components analysis (PCA) and partial least squares regression. The new method that can be used is lasso. Lasso has advantages in simultaneuosly controlling the variance of the fitted coefficients and performing automatic variable selection. Quantile regression is a method that can be us...

Statistical downscaling with generalized Pareto distribution (Study case: Extreme rainfall estimation)

Indonesia has tropical climate with small variation of temperature but quite large variation of rainfall. So the rainfall which is an essential climate element related to climate change has to be observed. Climate change may increase the incidence of extreme rainfall that affects flooding in farmland. In order to anticipate the occurrence of extreme rainfall, the information of rainfall forecast is required. Statistical Downscaling (SD) is a technique to model the relationship between global scale data and local scale data. Global Circulation Model (GCM) output is global scale data and rainfall is local scale data. GCM has characteristic non-linear, high dimension, and multicolinierity. These problem can be overcome by principal component analysis (PCA). One of the primary methods for estimating extreme rainfall is generalize Pareto distribution (GPD) regression based on a threshold. The objective of this study is SD modeling based on GPD to predict extreme rainfall. The result show that GPD models can pr...

Gamma distribution linear modeling with statistical downscaling to predict extreme monthly rainfall in Indramayu

Rainfall is an important factor in the agricultural process. Several methods to predict the rainfall have been carried out in Indonesia, such as the modeling of Statistical Downscaling (SDS). SDS models might involve ill-conditioned covariates (large dimension and high correlation/multi collinear). This problem could be solved by a variable selection technique such as L1 regularization/LASSO or a dimension reduction approach such as principal component analysis (PCA). In this paper, both methods were applied to generalized linear modeling with gamma distribution and compared in order to predict extreme monthly rainfall at 11 rain posts in Indramayu. Simulations were conducted to compare L1 regularization technique and principal component analysis in the prediction of responses. Two scenarios were based on the coefficient of beta and the distribution of response scenarios. The covariates used in this study were in observational data of GPCP version 2.2. The coefficient of beta scenarios were the combination of beta less than 1, equal 0, and greater than 1 vs all betas less than 1. Gamma distributions were used for distribution of response scenario with three different shape parameters. The simulation showed that L1 regularization technique resulted in almost better prediction than principal component analysis as the shape parameter was larger. The Root Mean Square Error (RMSE) of generalized linear model with Gamma distribution was less than that of principal component regression. However, all generalized linear models with Gamma distribution gave the smaller RMSE values for extreme value prediction above outliers. In this case, the quantiles, Q(0.90) and Q(0.95), were better prediction of extreme monthly rainfall.

Nested generalized linear mixed model with ordinal response: Simulation and application on poverty data in Java Island

The objective of this research is to build a nested generalized linear mixed model using an ordinal response variable with some covariates. There are three main jobs in this paper, i.e. parameters estimation procedure, simulation, and implementation of the model for the real data. At the part of parameters estimation procedure, concepts of threshold, nested random effect, and computational algorithm are described. The simulations data are built for 3 conditions to know the effect of different parameter values of random effect distributions. The last job is the implementation of the model for the data about poverty in 9 districts of Java Island. The districts are Kuningan, Karawang, and Majalengka chose randomly in West Java; Temanggung, Boyolali, and Cilacap from Central Java; and Blitar, Ngawi, and Jember from East Java. The covariates in this model are province, number of bad nutrition cases, number of farmer families, and number of health personnel. In this modeling, all covariates are grouped as ordin...