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Learning based fusion in ensembles for weather forecasting
conference contribution
posted on 2018-09-27, 00:00 authored by Ali Haidar, Brijesh VermaIn 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 LStart Page
72End Page
78Number of Pages
7Start Date
2017-07-29Finish Date
2017-07-31ISBN-13
9781538621653Location
Guilin, ChinaPublisher
IEEEPlace of Publication
Piscataway, NJ.Publisher DOI
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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)Usage metrics
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