Application of SVM optimized by genetic algorithm in forecasting and management of water consumption used in agriculture

Predication of water consumption used in agriculture is significant to set the planning of configuration optimization of water resources. In order to forecast water consumption used in agriculture exactly, support vector machine and genetic algorithm is proposed to forecast water consumption used in...

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Bibliographic Details
Main Authors: Li, You-zhu, Yang, Shan-shan
Format: Conference Proceeding
Language:English
Subjects:
Agriculture
Artificial neural networks
Economic forecasting
Educational institutions
forecasting water consumption
genetic algorithm
Genetic algorithms
Lagrangian functions
prediction capability
Predictive models
Resource management
support vector machine
Support vector machines
Water resources
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Summary:Predication of water consumption used in agriculture is significant to set the planning of configuration optimization of water resources. In order to forecast water consumption used in agriculture exactly, support vector machine and genetic algorithm is proposed to forecast water consumption used in agriculture, where genetic algorithm (GA) is used to select the parameters of support vector machine. The experimental results demonstrate that the proposed GA-SVM model provides better prediction capability and is therefore considered as a promising alternative method for forecasting water consumption used in agriculture.
DOI:10.1109/ICCAE.2010.5451325