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A novel approach for optimizing ensemble components in rainfall prediction

conference contribution
posted on 03.04.2019, 00:00 by Ali HaidarAli Haidar, Brijesh VermaBrijesh Verma, Toshi SinhaToshi Sinha
Precipitation is viewed as a complex phenomenon that influences the efficiency of the agricultural season. In this paper, an ensemble of neural networks has been created and optimized to estimate monthly rainfall for Innisfail, Australia. The proposed ensemble utilizes single neural networks as components and combines them using a neural network fusion method. A novel ensemble components selection approach has been proposed and deployed. Ensemble components were selected based on a hybrid Genetic Algorithm (GA) that combines standard GA with particle swarm optimization algorithm. Various statistical measurements were calculated to assess the accuracy of the proposed ensembles against single neural networks, climatology and ensembles generated through an alternative selection approach. A better performance was obtained with the proposed ensembles when compared to alternative models. © 2018 IEEE.

History

Start Page

1

End Page

8

Number of Pages

8

Start Date

08/07/2018

Finish Date

13/07/2018

ISBN-13

9781509060177

Location

Rio de Janeiro, Brazil

Publisher

IEEE

Place of Publication

Piscataway, NJ

Peer Reviewed

Yes

Open Access

No

Author Research Institute

Centre for Intelligent Systems

Era Eligible

Yes

Name of Conference

2018 IEEE Congress on Evolutionary Computation (CEC 2018)