Pumping optimization of coastal aquifers based on evolutionary algorithms and surrogate modular neural network models

Kourakos, G; Mantoglou, A

dc.contributor.author	Kourakos, G	en
dc.contributor.author	Mantoglou, A	en
dc.date.accessioned	2014-03-01T01:31:43Z
dc.date.available	2014-03-01T01:31:43Z
dc.date.issued	2009	en
dc.identifier.issn	0309-1708	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19909
dc.subject	Coastal aquifers	en
dc.subject	Evolutionary algorithms	en
dc.subject	Modular neural networks	en
dc.subject	Pumping optimization	en
dc.subject	Seawater intrusion	en
dc.subject	Surrogate models	en
dc.subject	Variable density models	en
dc.subject.classification	Water Resources	en
dc.subject.other	Aquifers	en
dc.subject.other	Backpropagation	en
dc.subject.other	Evolutionary algorithms	en
dc.subject.other	Groundwater geochemistry	en
dc.subject.other	Groundwater resources	en
dc.subject.other	Highway bridges	en
dc.subject.other	Hydrogeology	en
dc.subject.other	Mathematical models	en
dc.subject.other	Numerical methods	en
dc.subject.other	Optimization	en
dc.subject.other	Pumps	en
dc.subject.other	Salinity measurement	en
dc.subject.other	Seawater	en
dc.subject.other	Water wells	en
dc.subject.other	Wells	en
dc.subject.other	Coastal aquifers	en
dc.subject.other	Modular neural networks	en
dc.subject.other	Pumping optimization	en
dc.subject.other	Seawater intrusion	en
dc.subject.other	Surrogate models	en
dc.subject.other	Variable density models	en
dc.subject.other	Neural networks	en
dc.subject.other	algorithm	en
dc.subject.other	artificial neural network	en
dc.subject.other	coastal aquifer	en
dc.subject.other	groundwater flow	en
dc.subject.other	interpolation	en
dc.subject.other	numerical model	en
dc.subject.other	optimization	en
dc.subject.other	pumping	en
dc.subject.other	surrogate method	en
dc.subject.other	Cycladas	en
dc.subject.other	Eurasia	en
dc.subject.other	Europe	en
dc.subject.other	Greece	en
dc.subject.other	Santorin	en
dc.subject.other	Southern Aegean	en
dc.subject.other	Southern Europe	en
dc.title	Pumping optimization of coastal aquifers based on evolutionary algorithms and surrogate modular neural network models	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.advwatres.2009.01.001	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.advwatres.2009.01.001	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	Pumping optimization of coastal aquifers involves complex numerical models. In problems with many decision variables, the computational burden for reaching the optimal solution can be excessive. Artificial Neural Networks (ANN) are flexible function approximators and have been used as surrogate models of complex numerical models in groundwater optimization. However, this approach is not practical in cases where the number of decision variables is large, because the required neural network structure can be very complex and difficult to train. The present study develops an optimization method based on modular neural networks, in which several small subnetwork modules, trained using a fast adaptive procedure, cooperate to solve a complex pumping optimization problem with many decision variables. The method utilizes the fact that salinity distribution in the aquifer, depends more on pumping from nearby wells rather than from distant ones. Each subnetwork predicts salinity in only one monitoring well, and is controlled by relatively few pumping wells falling within certain control distance from the monitoring well. While the initial control area is radial, its shape is aclaptively improved using a Hermite interpolation procedure. The modular neural subnetworks are trained adaptively during optimization, and it is possible to retrain only the ones not performing well. As optimization progresses, the subnetworks are adapted to maximize performance near the current search space of the optimization algorithm. The modular neural subnetwork models are combined with an efficient optimization algorithm and are applied to a real coastal aquifer in the Greek island of Santorini. The numerical code SEAWAT was selected for solving the partial differential equations of flow and density dependent transport. The decision variables correspond to pumping rates from 34 wells. The modular subnetwork implementation resulted in significant reduction in CPU time and identified an even better solution than the original numerical model. (C) 2009 Elsevier Ltd. All rights reserved.	en
heal.publisher	ELSEVIER SCI LTD	en
heal.journalName	Advances in Water Resources	en
dc.identifier.doi	10.1016/j.advwatres.2009.01.001	en
dc.identifier.isi	ISI:000265330000003	en
dc.identifier.volume	32	en
dc.identifier.issue	4	en
dc.identifier.spage	507	en
dc.identifier.epage	521	en