DACE Toolbox for Kriging Modeling

In our research, we utilize kriging models to predict geological characteristics. Kriging is a spatial interpolation technique that assigns numerical attributes from sampled spatial points to unsampled locations. The model achieves this by modeling spatial correlations between sample points through variogram analysis and optimization algorithms. In our groundwater content prediction study, we implement kriging using the DACE toolbox's core functions like dacefit() for model training and predictor() for spatial predictions. The implementation involves configuring correlation functions (e.g., Gaussian or exponential) and optimizing parameters through maximum likelihood estimation. We incorporate multiple influencing factors including soil types and rainfall data as input variables, enhancing prediction accuracy through multivariate kriging approaches. The DACE toolbox provides essential methods for constructing the covariance matrix and solving the kriging system equations. Overall, the kriging model serves as an indispensable component in our research framework, enabling reliable spatial predictions with quantified uncertainty estimates.