IAHR, founded in 1935, is a worldwide independent member-based organisation of engineers and water specialists working in fields related to the hydro-environmental sciences and their practical application. Activities range from river and maritime hydraulics to water resources development and eco-hydraulics, through to ice engineering, hydroinformatics, and hydraulic machinery.
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You are here : eLibrary : IAHR World Congress Proceedings : 36th Congress - The Hague (2015) ALL CONTENT : Water resources and hydroinformatics : A system of hydrodynamic, water quality and neural network models for predicting water quality in th...
A system of hydrodynamic, water quality and neural network models for predicting water quality in the rio de la plata estuary
The R¨ªo de la Plata (RDLP) is a wide estuary formed by the confluence of the Paran¨¢ River delta and the Uruguay River
(total average flow of 22,000 m3/s). This estuary is the main source of drinking water for 11 million residents of Buenos
Aires city and its metropolitan. The RDLP coastal sector holds densely populated areas with tributaries running across
them. This is associated with the generation of pollution plumes on the estuary which can originate, under certain
specific hydrometeorological conditions, water quality deteriorating events that affect raw water at water intakes
(5,000,000_m3/day). In order to provide management support, the implementation of a raw water quality forecasting
system was decided. This paper describes the development and validation of that system, based on the integration of
different models with real-time sensors and weather forecast. This work also includes performance analysis of different
kinds of models (deterministic, stochastic and Artificial Neural Networks - ANN) to work as a system constituting a
prediction tool, and description of the finally adopted models, which are i) 1D Hydrodynamic / Water Quality Model of
the Paran¨¢ River and its Delta for predicting raw water turbidity, ii) a Back Propagation ANN model for the prediction of
RDLP tides at water intakes and iii) Kohonen ANN model for the prediction of ammonium in raw water. The validation of
this system of models is presented by the comparison of calculated and continuously measured Hydrodynamic and
quality parameters for at least a two-year period. This raw water quality forecasting system has already been
implemented and is at present running daily as a management support tool.
File Size : 2,455,797 bytes
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Chapter : IAHR World Congress Proceedings
Category : 36th Congress - The Hague (2015) ALL CONTENT
Article : Water resources and hydroinformatics
Date Published : 19/08/2015
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