Review: Simulation-optimization models for the management and monitoring of coastal aquifers

Abstract

The literature on the application of simulationoptimization approaches for management and monitoring of coastal aquifers is reviewed. Both sharp-and dispersive-interface modeling approaches have been applied in conjunction with optimization algorithms in the past to develop management solutions for saltwater intrusion. Simulation-optimization models based on sharp-interface approximation are often based on the Ghyben-Herzberg relationship and provide an efficient framework for preliminary designs of saltwater-intrusion management schemes. Models based on dispersiveinterface numerical models have wider applicability but are challenged by the computational burden involved when applied in the simulation-optimization framework. The use of surrogate models to substitute the physically based model during optimization has been found to be successful in many cases. Scalability is still a challenge for the surrogate modeling approach as the computational advantage accrued is traded-off with the training time required for the surrogate models as the problem size increases. Few studies have attempted to solve stochastic coastal-aquifer management problems considering model prediction uncertainty. Approaches that have been reported in the wider groundwater management literature need to be extended and adapted to address the challenges posed by the stochastic coastal-aquifer management problem. Similarly, while abundant literature is available on simulation-optimization methods for the optimal design of groundwater monitoring networks, applications targeting coastal aquifer systems are rare. Methods to optimize compliance monitoring strategies for coastal aquifers need to be developed considering the importance of monitoring feedback information in improving the management strategies.

Preliminary Hydrogeologic Modeling and Optimal Monitoring Network Design for a Contaminated Abandoned Mine Site Area: Application of Developed Monitoring Network Design Software

“…The performance evaluation results show that the developed methodology is successful in developing an optimal monitoring plan for the contaminant concentration values in a highly complex groundwater pollution scenario. The developed software package CARE-GWMND [38] is effective in finding the solution results for optimal monitoring network design, and it may be possible to adopt the methodology and the software to various other complex real life scenarios of groundwater pollution. Further studies for incorporating more complex reactive multiple species geochemical processes in the optimal monitoring network design for a mine site are being carried out now.…”

“…However, these monitoring networks were static in nature. A number of methodologies have been proposed using different optimization algorithms for improving the monitoring network design results as reported in [37] [38].…”

Linking a Simulated Annealing Based Optimization Model with PHT3D Simulation Model for Chemically Reactive Transport Processes to Optimally Characterize Unknown Contaminant Sources in a Former Mine Site in Australia

“…Other methodologies proposed latter include: determining optimal estimates of the unknown model parameters and source characteristics using nonlinear maximum likelihood estimation based inverse models [29]; source identification using embedded nonlinear optimization technique [30] [44].…”

Application of artificial neural network coupled with genetic algorithm and simulated annealing to solve groundwater inflow problem to an advancing open pit mine

“…Recently GA and SA have been combined with neural methods to avoid drawbacks of local minimization methods. Many other global minimization methods are suitable for that purpose, although they are used rather rarely in this context (Duch and Korczak, 1998;Bhattacharjya and Datta, 2009;Yeh, 2015;Sreekanth and Datta, 2015). The coupled ANN-GA and ANN-SA algorithms have a great potential to handle problems such as optimization in complicated nonlinear systems.…”