IDENTIFICATION OF POLLUTANT SOURCE CHARACTERISTICS UNDER UNCERTAINTY IN CONTAMINATED WATER RESOURCES SYSTEMS USING ADAPTIVE SIMULATED ANEALING AND FUZZY LOGIC

Abstract

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.