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Learning based fusion in ensembles for weather forecasting

conference contribution
posted on 27.09.2018, 00:00 by Ali HaidarAli Haidar, Brijesh VermaBrijesh Verma
In this study, a novel ensemble is proposed to forecast monthly rainfall for sugarcane areas in Queensland, Australia. Multiple ensembles of neural networks have been developed to estimate the amount of monthly rainfall for Innisfail, Queensland Australia. Furthermore, four fusion methods such as average fusion, neural network learning fusion, lowest error based neural network fusion and neural network based particle swarm optimization fusion were proposed and evaluated. The obtained models were compared against alternative models and climatology where results revealed higher accuracy with ensemble generated outlooks. Among the ensembles with four fusion methods, an ensemble of feed forward neural networks using resilient backpropagation algorithm and particle swarm optimization produced the highest accuracy with 166.71 mms Root Mean Square Error (RMSE). © 2017 IEEE.

History

Editor

Lui Y; Zhao L; Cai G; Xiao G; Li K; Wang L

Start Page

72

End Page

78

Number of Pages

7

Start Date

29/07/2017

Finish Date

31/07/2017

ISBN-13

9781538621653

Location

Guilin, China

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

13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2017)