Evaluation of models generated via hybrid evolutionary algorithms for the prediction of Microcystis concentrations in the Vaal Dam, South Africa

Abstract

Cyanobacteria are responsible for many problems in drinking water treatment works (DWTW) because of their ability to produce cyanotoxins that potentially can have an adverse effect on consumer health. Therefore, the monitoring of cyanobacteria in source waters entering DWTW has become an essential part of drinking water treatment management. Managers of DWTW rely heavily on results from physical, chemical and biological water quality analyses, from grab samples, for their management decisions. However, results of water quality analyses may be delayed from 3 h to 14 days depending on a magnitude of factors such as sampling, distance and accessibility to laboratory, laboratory sample turnaround times, specific methods used in analyses, etc. Therefore, the benefit to managers and production chemists to be able to forecast future events of high cyanobacterial cell concentrations in the source water is evident.

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