Development of a clusterwise-linear-regression-based forecasting system for characterizing DNAPL dissolution behaviors in porous media

Groundwater contamination by dense non-aqueous phase liquids (DNAPLs) has become an issue of great concern in many industrialized countries due to their serious threat to human health. Dissolution and transport of DNAPLs in porous media are complicated, multidimensional and multiphase processes, which pose formidable challenges for investigation of their behaviors and implementation of effective remediation technologies. Numerical simulation models could help gain in-depth insight into complex mechanisms of DNAPLs dissolution and transport processes in the subsurface; however, they were computationally expensive, especially when a large number of runs were required, which was considered as a major obstacle for conducting further analysis. Therefore, proxy models that mimic key characteristics of a full simulation model were desired to save many orders of magnitude of computational cost. In this study, a clusterwise-linear-regression (CLR)-based forecasting system was developed for establishing a statistical relationship between DNAPL dissolution behaviors and system conditions under discrete and nonlinear complexities. The results indicated that the developed CLR-based forecasting system was capable not only of predicting DNAPL concentrations with acceptable error levels, but also of providing a significance level in each cutting/merging step such that the accuracies of the developed forecasting trees could be controlled. This study was a first attempt to apply the CLR model to characterize DNAPL dissolution and transport processes.