Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling

作者：Leah L. Rogers, Farid Dowla · 发表于：Water Resources Research · 年份：1994 · DOI：10.1029/93wr01494 · 被引用次数：490 · 研究领域：Groundwater flow and contamination studies、Geophysical and Geoelectrical Methods、Stochastic Gradient Optimization Techniques

A new approach to nonlinear groundwater management methodology is presented which optimizes aquifer remediation with the aid of artificial neural networks (ANNs). The methodology allows solute transport simulations, usually the main computational component of management models, to be run in parallel. The ANN technology, inspired by neurobiological theories of massive interconnection and parallelism, has been successfully applied to a variety of optimization problems. In this new approach, optimal management solutions are found by (1) first training an ANN to predict the outcome of the flow and transport code, and (2) then using the trained ANN to search through many pumping realizations to find an optimal one for successful remediation. The behavior of complex groundwater scenarios with spatially variable transport parameters and multiple contaminant plumes is simulated with a two‐dimensional hybrid finite‐difference/finite‐element flow and transport code. The flow and transport code develops the set of examples upon which the network is trained. The input of the ANN characterizes the different realizations of pumping, with each input indicating the pumping level of a well. The output is capable of characterizing the objectives and constraints of the optimization, such as attainment of regulatory goals, value of cost functions and cleanup time, and mass of contaminant removal. The supervised learning algorithm of back propagation was used to train the network. The conjugate g...