Abstract: Water resources optimization models often use spatial numerical models to approximate the physics of natural systems. The discretization of the numerical grids can affect their search for optimal solutions, in terms of ...

Abstract: This paper presents a multiscale derivative method for solving a successive approximation linear quadratic regulator model for optimal in situ bioremediation design. An efficient one-sided forward divided difference numerical ...

Abstract: Solving field-scale optimal groundwater remediation design problems is a challenge, especially when computationally intensive reactive transport models are needed. In this paper, a full multiscale approach to partial ...

Abstract: Genetic algorithms allow solution of more complex, nonlinear civil, and environmental engineering problems than traditional gradient-based approaches, but they are more computationally intensive. One way to improve algorithm ...

Abstract: The interactive multiobjective genetic algorithm (IMOGA) is a promising new approach to calibrate models. The IMOGA combines traditional optimization with an interactive framework, thus allowing both quantitative calibration ...

Abstract: Ground-water management models can be computationally intensive and field-scale application of these techniques to complex processes such as in situ bioremediation is not currently possible. A one-way spatial multiscale ...

Abstract: This study demonstrates the use of high-order Pareto optimization (i.e., optimizing a system for more than two objectives) on a long-term monitoring (LTM) application. The LTM application combines quantile kriging and the ...

Abstract: A feasibility study of a new technology for remediating sites contaminated with dense non-aqueous-phase liquids (DNAPLs) is presented. The technology combines two conventional remediation approaches, thermal treatment and ...

Abstract: Optimal groundwater remediation design problems are often complex, nonlinear, and computationally intensive. Genetic algorithms allow solution of more complex nonlinear problems than traditional gradient-based approaches, ...