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

journal contribution
posted on 10.05.2018, 00:00 authored by Ali HaidarAli Haidar, Brijesh VermaBrijesh Verma
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.

History

Volume

22

Issue

24

Start Page

8113

End Page

8130

Number of Pages

12

eISSN

1433-7479

ISSN

1432-7643

Publisher

Springer, Germany

Peer Reviewed

Yes

Open Access

No

Author Research Institute

Centre for Intelligent Systems

Era Eligible

Yes

Journal

Soft Computing