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A novel approach for optimizing climate features and network parameters in rainfall forecasting
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
22Issue
24Start Page
8113End Page
8130Number of Pages
12eISSN
1433-7479ISSN
1432-7643Publisher
Springer, GermanyPublisher DOI
Peer Reviewed
- Yes
Open Access
- No
Author Research Institute
- Centre for Intelligent Systems
Era Eligible
- Yes