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 : 32nd Congress - Venice (2007) : THEME B: Data Acquisition and Processing For Scientific Knowledge and Public Awareness. : Modular data-driven hydrologic models with incorporated knowledge: neural networks and model trees
Modular data-driven hydrologic models with incorporated knowledge: neural networks and model trees
Author : Gerald Corzo, Michael Siek, Roland Price, Dimitri Solomatine
Hydraulic phenomena are composed of a number of interacting sub-processes, so one single model handling all processes is often inaccurate. Modular models allow for modelling subprocessesseparately. If data-driven modular models are built, they allow for incorporation of domain knowledge and thus help break down the barriers associated with the "black-box nature" of such models. In this paper we compare two types of modular models that incorporate hydrological knowledge into the modularization process: based on artificial neural networks (ANN), and M5 model trees. The latter have accuracy similar to that of modular models based ANN models, however they can be easier interpreted and are faster. The best performance is obtained from the modular models taking into account the hydrological knowledge.
File Size : 215,621 bytes
File Type : Adobe Acrobat Document
Chapter : IAHR World Congress Proceedings
Category : 32nd Congress - Venice (2007)
Article : THEME B: Data Acquisition and Processing For Scientific Knowledge and Public Awareness.
Date Published : 01/07/2007
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