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Water Resources Management
Published in: Water Resources Management 15/2015

01-12-2015

Irrigation Demand Forecasting Using Artificial Neuro-Genetic Networks

Authors: R. González Perea, E. Camacho Poyato, P. Montesinos, J. A. Rodríguez Díaz

Published in: Water Resources Management | Issue 15/2015

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Abstract

In recent years, a significant evolution of forecasting methods has been possible due to advances in artificial computational intelligence. The achievement of the optimal architecture of an ANN is a complex process. Thus, in this work, an Evolutionary Robotic (study of the evolution of an ANN using Genetic Algorithm) approach has been used to obtain an Artificial Neuro-Genetic Networks (ANGN) to the short-term forecasting of daily irrigation water demand that maximizes the accuracy of the predictions. The methodology is applied in the Bembézar Irrigation District (Southern Spain). An optimal ANGN architecture (ANGN (7, 29, 16, 1)) has achieved obtaining a Standard Error Prediction (SEP) value of the daily water demand of 12.63 % and explaining 93 % of the total variance observed during validation process. The developed model proved to be a powerful tool that, without long dataset and time requirements, can be very useful for the development of management strategies.
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Metadata
Title
Irrigation Demand Forecasting Using Artificial Neuro-Genetic Networks
Authors
R. González Perea
E. Camacho Poyato
P. Montesinos
J. A. Rodríguez Díaz
Publication date
01-12-2015
Publisher
Springer Netherlands
Published in
Water Resources Management / Issue 15/2015
Print ISSN: 0920-4741
Electronic ISSN: 1573-1650
DOI
https://doi.org/10.1007/s11269-015-1134-4

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