(Q57571807)

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Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models

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Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models (English)

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Vazken Andréassian

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Charles Perrin

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Charles Perrin

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2

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author name string

François Anctil

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1

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publication date

April 2004

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published in

Environmental Modelling and Software

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volume

19

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issue

4

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page(s)

357-368

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cites work

Approximation by superpositions of a sigmoidal function

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7 January 2021

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An artificial neural network approach to rainfall-runoff modelling

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Finding Structure in Time

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Training feedforward networks with the Marquardt algorithm.

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Multilayer feedforward networks are universal approximators

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How much complexity is warranted in a rainfall-runoff model?

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Bayesian Interpolation

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Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications

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River flow forecasting through conceptual models part I — A discussion of principles

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Natural Evaporation from Open Water, Bare Soil and Grass

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Does a large number of parameters enhance model performance? Comparative assessment of common catchment model structures on 429 catchments

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Automatic early stopping using cross validation: quantifying the criteria

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Evaluation of Maximum Likelihood Parameter estimation techniques for conceptual rainfall-runoff models: Influence of calibration data variability and length on model credibility

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Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data

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DOI

10.1016/S1364-8152(03)00135-X

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DBLP publication ID

journals/envsoft/AnctilPA04

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DBLP Dataset 2021-01-02

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28 January 2021

(Q57571807)

Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models

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