Modified Genetic Algorithm in Isotropic Semivariogram Modeling: A Case Study of Groundwater Level in Kalimantan Peatland

The purpose of this study is to develop a method to estimate semivariogram parameters using genetic algorithm (GA). GA is a numerical method that has been extensively applied. GA is applicable to estimate semivariogram parameters including constraint based on the parameters. The modification that applied to GA shows better performance than iterative least square (ILS). The application of spatial analysis to groundwater level (GWL) in peatland areas is still limited, especially semivariogram analysis. Thus, the m-GA (modified-GA) is applied to GWL in Kalimantan peatland and then compared with ILS. The study shows that the spherical semivariogram model estimated using the m-GA provides the best performance, because both the model and kriging have the lowest root mean square error (RMSE) values, at  and , respectively. The combination of spherical semivariogram model with the m-GA produces optimal and accurate semivariogram parameters to support kriging interpolation on GWL peatland.