Artificial recharge is used to increase the availability of groundwater storage and reduce saltwater intrusion in coastal aquifers, where pumping and droughts have severely impaired groundwater quality. The implementation of optimal recharge methods requires knowledge of physical, chemical, and biological phenomena involving water and wastewater filtration in the subsoil, together with engineering aspects related to plant design and maintenanceoperations.This studyuses anovel Decision Support System(DSS),whichincludessoil aquifer treatment (SAT) evaluation, to design an artificial recharge plant. The DSS helps users make strategic decisions on selecting the most appropriate recharge methods and water treatment technologies at specific sites. This will enable the recovery of safe water using managed aquifer recharge (MAR) practices, and result in reduced recharge costs. The DSS was built using an artificial intelligence technique and knowledge-based technology, related to both quantitative and qualitative aspects of water supply for artificial recharge. The DSS software was implemented using rules based on the cumulative experience of wastewater treatment

A Suitable Tool for Sustainable Groundwater Management

I. S. Liso
2017

Abstract

Artificial recharge is used to increase the availability of groundwater storage and reduce saltwater intrusion in coastal aquifers, where pumping and droughts have severely impaired groundwater quality. The implementation of optimal recharge methods requires knowledge of physical, chemical, and biological phenomena involving water and wastewater filtration in the subsoil, together with engineering aspects related to plant design and maintenanceoperations.This studyuses anovel Decision Support System(DSS),whichincludessoil aquifer treatment (SAT) evaluation, to design an artificial recharge plant. The DSS helps users make strategic decisions on selecting the most appropriate recharge methods and water treatment technologies at specific sites. This will enable the recovery of safe water using managed aquifer recharge (MAR) practices, and result in reduced recharge costs. The DSS was built using an artificial intelligence technique and knowledge-based technology, related to both quantitative and qualitative aspects of water supply for artificial recharge. The DSS software was implemented using rules based on the cumulative experience of wastewater treatment
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/231319
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