Identification of pollutant source characteristics under uncertainty in contaminated water resources systems using adaptive Simulated Anealing and fuzzy logic

Effective environmental management and remediation strategies are required to remediate contaminated water resources. Accurate characterizing of unknown contaminant sources is vital for selection of appropriate environmental management plan and reduction of long term remedial costs. In order to characterize the sources of contamination, the aquifer boundary conditions and hydrogeologic parameter values need to be estimated or specified. In real life contaminated aquifers, often there are sparse and inaccurate information available. On the other hand, extensive collection of data is very costly. The uncertain and highly variable natures of water resources systems affect the accuracy of contaminant source identification models. In this study, an optimal source identification model incorporating Adaptive Simulated Annealing optimization algorithm linked with the numerical flow and transport simulation models, is designed to identify contaminant source characteristics. The fuzzy logic concept is used to identify the effect of hydrogeological parameter uncertainty on groundwater flow and transport simulation. The fuzzy membership values incorporate the reliability of specified parameter values in to the optimization model. An illustrative study area is used to show the potential applicability of the proposed methodology. The incorporation of fuzzy logic in source identification model increases the applicability of contaminant source detection models in real-life contaminated water resources systems