We evaluate the performance and statistical accuracy of the fast Fourier transform method for unconditional and conditional simulation. The method is applied under difficult but realistic circumstances of a large field (1001 by 1001 points) with abundant conditioning criteria and a band limited, anisotropic, fractal-based statistical characterization (the von Kármán model). The simple Fourier u...

This paper is continuation work of a research started in 2005. In the previous research, variety of fuzzy digital elevation models was constructed and the best fit model was selected in order to present an alternative in modelling height information. The previous study revealed that fuzzy digital elevation model gave satisfied result compared with the result from TIN (Triangulated Irregular Net...

Nowadays, kriging has been accepted as the most common method of grade estimation in mineral resource evaluation stage. Access to the crisp assay data and a variogram model are the necessary means for the utilization of this method. Since fitting a crisp variogram model is generally difficult, if not impossible, the fitted theoretical model is usually tainted with uncertainty due to various rea...

This study discusses the development of a prediction model for classification rainfall based on time in Java. The method used this research is naive Bayes and simple kriging. Naive prediction, while kriging an interpolation mapping. There are two scenarios used, that building daily monthly classification, with data taken from 27 weather stations island Java 2010 to 2021. results obtained proces...

Soil property mapping is commonly done in two dimensions, and variation by depth is ignored. The only way to consider the vertical variation is mapping in 3D. In this paper, we present a method based on polynomial depth functions. The objective is determining the effectiveness of a simple polynomial to predict cone-index data by depth over a field. In order to study the performances of the prop...

The high computational cost of population based optimization methods, such as multiobjective genetic algorithms (MOGAs), has been preventing applications of these methods to realistic engineering design problems. The main challenge is to devise methods that can significantly reduce the number of simulation (objective/constraint functions) calls. We present a new multi-objective design optimizat...

When analyzing data from computationally expensive simulation codes, surrogate modeling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualization and optimization. Kriging is a popular surrogate modeling technique used for the Design and Analysis of Computer Experiments (DACE). Hence, the past decade Kriging has been the subject of extensiv...

Kriging is a useful method for developing metamodels for product design optimization. The most popular kriging method, known as ordinary kriging, uses a constant mean in the model. In this article, a modified kriging method is proposed, which has an unknown mean model. Therefore it is called blind kriging. The unknown mean model is identified from experimental data using a Bayesian variable sel...

This paper investigates the benefits of integrating coarse resolution satellite-derived precipitation estimates with quasi-point rain gauge data for generating a fine spatial resolution precipitation map product. To integrate the two precipitation data sources, a geostatistical downscaling and integration approach is presented that can account for the differences in spatial resolution between d...