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UNORGANIZED MACHINES FOR SEASONAL STREAMFLOW SERIES FORECASTING

Department of Systems Engineering, School of Electrical and Computer Engineering, University of Campinas, Av. Albert Einstein, 400, Campinas, São Paulo 13083-852, Brazil

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LEVY BOCCATO

Department of Computer Engineering and Industrial Automation, School of Electrical and Computer Engineering, University of Campinas, Av. Albert Einstein, 400, Campinas, São Paulo 13083-852, Brazil

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ROMIS ATTUX

Department of Computer Engineering and Industrial Automation, School of Electrical and Computer Engineering, University of Campinas, Av. Albert Einstein, 400, Campinas, São Paulo 13083-852, Brazil

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CHRISTIANO LYRA

Department of Systems Engineering, School of Electrical and Computer Engineering, University of Campinas, Av. Albert Einstein, 400, Campinas, São Paulo 13083-852, Brazil

Corresponding author.

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https://doi.org/10.1142/S0129065714300095Cited by:41 (Source: Crossref)

Abstract

Modern unorganized machines — extreme learning machines and echo state networks — provide an elegant balance between processing capability and mathematical simplicity, circumventing the difficulties associated with the conventional training approaches of feedforward/recurrent neural networks (FNNs/RNNs). This work performs a detailed investigation of the applicability of unorganized architectures to the problem of seasonal streamflow series forecasting, considering scenarios associated with four Brazilian hydroelectric plants and four distinct prediction horizons. Experimental results indicate the pertinence of these models to the focused task.

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Vol. 24, No. 03

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Accepted 23 December 2013

Published: 10 February 2014

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System Upgrade on Tue, May 28th, 2024 at 2am (EDT)

UNORGANIZED MACHINES FOR SEASONAL STREAMFLOW SERIES FORECASTING

Abstract

References

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System Upgrade on Tue, May 28th, 2024 at 2am (EDT)

UNORGANIZED MACHINES FOR SEASONAL STREAMFLOW SERIES FORECASTING

Abstract

Recommended

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