Management of groundwater contamination often involves conflicting objectives and substantial uncertainty. The primary objective of this paper is to present a new approach, called the probabilistic multiobjective genetic algorithm (PMOGA), which incorporates uncertainty with multiobjective Pareto optimization. This type of approach is needed for water resources problems with multiple uncertain objectives; previous uncertainty-based optimization approaches consider uncertainty only in the constraints (typically constraining reliability with respect to environmental standards) or in a single objective. The optimization algorithm uses a probabilistic ranking and crowding scheme that improves decision making within the genetic algorithm by identifying a set of solutions that are Pareto optimal despite the uncertainty in the objective values. The algorithm is applied to two groundwater remediation test cases with uncertain objectives: a hypothetical case study and a field-scale pump-and-treat design problem at the Umatilla Chemical Depot situated at Hermiston, Oregon. For these case studies the primary source of uncertainty stems from uncertain aquifer hydraulic conductivity values, which affect both the remediation cost and efficiency. Results are analyzed, and the advantages of the PMOGA are discussed relative to an averaging-based multiobjective approach, a stochastic single-objective approach (similar to chance constrained optimization), and a deterministic multiobjective approach. The results demonstrate that using such an uncertainty-based multiobjective optimization scheme can give valuable information about remediation options, giving results that are not only cost effective but that also have lower uncertainty.
ASJC Scopus subject areas
- Water Science and Technology