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A novel approach for optimizing climate features and network parameters in rainfall forecasting

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Abstract

Artificial neural networks are widely applied for different forecasting applications including rainfall forecasting. The climate input features and parameters for neural networks highly affect the overall performance of the prediction model. Therefore, an appropriate approach for the selection of features and parameters is needed. In this paper, a novel approach is proposed to select the input features and neural network parameters. A hybrid genetic algorithm that combines natural reproduction and particle swarm optimization characteristics was developed to select the best input features and network parameters for each month. The developed model was compared against alternative climate and network parameters feature selection model, climate feature selection model and climatology where a better accuracy was recorded with the proposed model. The skill score against the three alternative climate models was 17.41, 21.68 and 32.12%, respectively. The aggregated time series of the proposed model showed a root-mean-square error of 141.67 mm for a location with 3553.00 mm annual average.

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Acknowledgements

This study was funded by CQU Research Division.

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Authors and Affiliations

  1. Centre for Intelligent Systems, School of Engineering and Technology, Central Queensland University, 160 Ann Street, Brisbane, QLD, 4000, Australia

    Ali Haidar & Brijesh Verma

Authors
  1. Ali Haidar
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  2. Brijesh Verma
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Correspondence to Ali Haidar.

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Ali Haidar declares that he has no conflict of interest. Brijesh Verma declares that he has no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by V. Loia.

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Haidar, A., Verma, B. A novel approach for optimizing climate features and network parameters in rainfall forecasting. Soft Comput 22, 8119–8130 (2018). https://doi.org/10.1007/s00500-017-2756-7

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  • DOI: https://doi.org/10.1007/s00500-017-2756-7

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