Parameter estimation using artificial neural network and genetic algorithm for free-product migration and recovery

Artificial neural network (ANN) is considered to be a universal function approximator, and genetic algorithm (GA) is considered to be a robust optimization technique. As such, ANN regression analysis and ANN-GA optimization techniques can be used to perform inverse groundwater modeling for parameter estimation. In this manuscript the applicability of these two techniques in solving an inverse problem related to a light-hydrocarbon-contaminated site is assessed. The critical parameters to be evaluated are grain-size distribution index a and saturated hydraulic conductivity of water K-SW, since these parameters control free-product volume predictions and flow. A set of published data corresponding to a light-hydrocarbon-contaminated unconfined aquifer was used as the base case to determine the applicability of these methods under a variety of scenarios. Using limited monitoring-and recovery-well data under homogeneous and heterogeneous conditions, the critical parameters were evaluated. The results were used to determine the relative effectiveness of each method and corresponding limitations. The results of the work suggested that ANN regression analysis has limited utility, especially with heterogeneous soils, whereas the ANN-GA optimization can provide superior results with better computational efficiency. Finally, a general guideline for solving inverse problems using the two techniques is outlined.