Optimal Field-Scale Groundwater Remediation Using Neural Networks and the Genetic Algorithm

Abstract

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Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks

“…Historically, ANNs have been successfully applied to several water resource problems, such as groundwater remediation designs (Rizzo and Dougherty 1996;Rogers et al 1995;Aly and Peralta 1999;Yan and Minsker 2005). In a recently developed application, Yan and Minsker (2006) reported a model for groundwater remediation design that makes use of an adaptive neural network and a single objective genetic algorithm.…”

Optimal control algorithm and neural network for dynamic groundwater management

“…Recently, many researchers have successfully applied ANN models in hydrologic modelling, such as typhoon rainfall forecasting (Lin and Chen, 2005), the determination of aquifer parameters (Samani et al, 2007), and regional ground water levels simulation (Coppola et al, 2003a(Coppola et al, ,b, 2005Feng et al, 2008). A number of studies combine the optimization model with ANN (Rogers and Dowla, 1994;Rogers et al, 1995;Johnson and Rogers, 2001;Rao et al, 2003Rao et al, , 2005. Rogers and Dowla (1994) used ANN-GA methodology to replace 2766 H.-J.…”

“…The model uses 20 pre-selected extraction locations with steadystate pumping rate to search for the subset producing the smallest volume of pumping water over a 40-year planning period. Rogers et al (1995) also used ANN-GA methodology to locate the best pumping patterns for meeting remediation objectives. Rao et al (2003) apply simulated annealing (SA) with an ANN, which replaces the SHARP (A numerical finite-difference model to simulate freshwater and saltwater flow separated by a sharp interface) interface flow model to meet demand during non-monsoon season without including excessive saltwater instruction.…”

Multi-Objective Conjunctive Use Optimization

“…Consequently, many researchers investigated the use of alternative optimization techniques for groundwater management. Such methods include simulated annealing (Rizzo and Dougherty, 1994), artificial neural networks (Rogers and Dowla, 1994;Rogers et. al., 1995;SSOL, 2003), and genetic algorithms (McKinney and Lin, 1994;Ritzel et.…”