Presenter/Author Information

Stefano Marsili-Libelli
G. Pacini
C. Barresi

Keywords

eutrophication, fuzzy systems, pattern recognition, decision support systems

Start Date

1-7-2002 12:00 AM

Description

The Orbetello lagoon is a shallow brackish waterbody subject to intense and diverse eutrophication (phytoplancton, macroalgae and macrophytes). Periodically a large amount of algae must be artificially removed, their collection and disposal representing a considerable management cost. This paper describes the design of a bloom predictor based on the daily fluctuations of simple water quality parameters such ad dissolved oxygen, oxidation-reduction potential, pH and temperature. The task of the fuzzy predictor is to recognise the possibility that a bloom of the macroalgae population is about to occur based on the changing daily pattern of these variables. The fuzzy predictor is based on a number of fuzzy rules derived from experimental observations and expert knowledge. A whole year of hourly data was analysed and used to form the initial knowledge-base. The tests show that the inferential engine has good predictive capabilities, which could be improved when more data becoming available.

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Jul 1st, 12:00 AM

Fuzzy Prediction of the Algal Blooms in the Orbetello Lagoon

The Orbetello lagoon is a shallow brackish waterbody subject to intense and diverse eutrophication (phytoplancton, macroalgae and macrophytes). Periodically a large amount of algae must be artificially removed, their collection and disposal representing a considerable management cost. This paper describes the design of a bloom predictor based on the daily fluctuations of simple water quality parameters such ad dissolved oxygen, oxidation-reduction potential, pH and temperature. The task of the fuzzy predictor is to recognise the possibility that a bloom of the macroalgae population is about to occur based on the changing daily pattern of these variables. The fuzzy predictor is based on a number of fuzzy rules derived from experimental observations and expert knowledge. A whole year of hourly data was analysed and used to form the initial knowledge-base. The tests show that the inferential engine has good predictive capabilities, which could be improved when more data becoming available.