Comparative evaluation of genetic programming and neural network as potential surrogate models for coastal aquifer management
Sreekanth, J., and Datta, Bithin (2011) Comparative evaluation of genetic programming and neural network as potential surrogate models for coastal aquifer management. Water Resource Management, 25 (13). pp. 3201-3218.
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Determining the optimal rates of groundwater extraction for the sustainable use of coastal aquifers is a complex water resources management problem. It necessitates the application of a 3D simulation model for coupled flow and transport simulation together with an optimization algorithm in a linked simulation-optimization framework. The use of numerical models for aquifer simulation within optimization models is constrained by the huge computational burden involved. Approximation surrogates are widely used to replace the numerical simulation model, the widely used surrogate model being Artificial Neural Networks (ANN). This study evaluates genetic programming (GP) as a potential surrogate modeling tool and compares the advantages and disadvantages with the neural network based surrogate modeling approach. Two linked simulation optimization models based on ANN and GP surrogate models are developed to determine the optimal groundwater extraction rates for an illustrative coastal aquifer. The surrogate models are linked to a genetic algorithm for optimization. The optimal solutions obtained using the two approaches are compared and the advantages of GP over the ANN surrogates evaluated.
|Item Type:||Article (Refereed Research - C1)|
|Keywords:||coastal aquifer, management, optimization, surrogate model, genetic programming, ANN, genetic algorithm|
|Date Deposited:||28 Mar 2012 04:53|
|FoR Codes:||09 ENGINEERING > 0905 Civil Engineering > 090509 Water Resources Engineering @ 50%
09 ENGINEERING > 0907 Environmental Engineering > 090799 Environmental Engineering not elsewhere classified @ 50%
|SEO Codes:||96 ENVIRONMENT > 9609 Land and Water Management > 960999 Land and Water Management of Environments not elsewhere classified @ 100%|
|Citation Count from Web of Science||