Practical Implementation of New Particle Tracking Method to the Real Field of Groundwater Flow and Transport

In articles published in 2009 and 2010, Suk and Yeh reported the development of an accurate and efficient particle tracking algorithm for simulating a path line under complicated unsteady flow conditions, using a range of elements within finite elements in multidimensions. Here two examples, an aquifer storage and recovery (ASR) example and a landfill leachate migration example, are examined to enhance the practical implementation of the proposed particle tracking method, known as Suk's method, to a real field of groundwater flow and transport. Results obtained by Suk's method are compared with those obtained by Pollock's method. Suk's method produces superior tracking accuracy, which suggests that Suk's method can describe more accurately various advection-dominated transport problems in a real field than existing popular particle tracking methods, such as Pollock's method. To illustrate the wide and practical applicability of Suk's method to random-walk particle tracking (RWPT), the original RWPT has been modified to incorporate Suk's method. Performance of the modified RWPT using Suk's method is compared with the original RWPT scheme by examining the concentration distributions obtained by the modified RWPT and the original RWPT under complicated transient flow systems

Support Vector Machines (SVMs) for Monitoring Network Design

In this paper we present a hydrologic application of a new statistical learning methodology called support vector machines (SVMs). SVMs are based on minimization of a bound on the generalized error (risk) model, rather than just the mean square error over a training set. Due to Mercer\u27s conditions on the kernels, the corresponding optimization problems are convex and hence have no local minima. In this paper, SVMs are illustratively used to reproduce the behavior of Monte Carlo–based flow and transport models that are in turn used in the design of a ground water contamination detection monitoring system. The traditional approach, which is based on solving transient transport equations for each new configuration of a conductivity field, is too time consuming in practical applications. Thus, there is a need to capture the behavior of the transport phenomenon in random media in a relatively simple manner. The objective of the exercise is to maximize the probability of detecting contaminants that exceed some regulatory standard before they reach a compliance boundary, while minimizing cost (i.e., number of monitoring wells). Application of the method at a generic site showed a rather promising performance, which leads us to believe that SVMs could be successfully employed in other areas of hydrology. The SVM was trained using 510 monitoring configuration samples generated from 200 Monte Carlo flow and transport realizations. The best configurations of well networks selected by the SVM were identical with the ones obtained from the physical model, but the reliabilities provided by the respective networks differ slightly