Metaheuristic kriging: A new spatial estimation method

Authors : Fırat ATALAY, Güneş ERTUNÇ

Pages : 483-492

View : 18 | Download : 5

Publication Date : 2017-06-01

Article Type : Research Paper

Abstract :Kriging is one of the most widely used spatial estimation method. In kriging estimation, weights assigned to the neighboring data are determined by minimizing the estimation error variance insert ignore into journalissuearticles values(EEV);. Due to the minimization of the EEV the variability of the estimation result is lower than the original data. This paper presents the metaheuristic kriging insert ignore into journalissuearticles values(MK); as a new estimation method which has similar structure with kriging. But unlike kriging MK does not minimize the estimation error variance, instead converges to the EEV minimum which provides MK to increase the variability of the estimation. The MK uses the metaheuristic differential evolution algorithm in minimization of the EEV which gives names the MK. As a case study, Ordinary kriging insert ignore into journalissuearticles values(OK); and MK are applied to the Jura data set to estimate the spatial distribution of the Nickel insert ignore into journalissuearticles values(Ni); content. Results of the estimations are compared. Results shows that metaheuristic kriging over performed to the ordinary kriging in terms of variability of the estimation. The MK can be used any place where kriging is applied due to the variability of the estimation is higher than OK. The parameters used in MK are case specific so parameter tuning have to be made in the estimations to reach the desired outcomes. This study only exposes the univariate spatial estimation.
Keywords : Kriging, Metaheuristic Kriging, Differential Evolution, Spatial Estimation, Optimization