Anzeige
Erschienen in:
2014 | OriginalPaper | Buchkapitel
Selecting Most Suitable Members for Neural Network Ensemble Rainfall Forecasting Model
verfasst von : Harshani Nagahamulla, Uditha Ratnayake, Asanga Ratnaweera
Erschienen in: Recent Advances on Soft Computing and Data Mining
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Neural network ensembles are more accurate than a single neural network because they have higher generalization ability. To increase the generalization ability the members of the ensemble must be accurate and diverse. This study presents a method for selecting the most suitable members for an ensemble which uses genetic algorithms to minimize the error function of the ensemble ENN-GA. The performance of the proposed method is compared with the performance of two widely used methods, bagging and boosting. The models developed are trained and tested using 41 years rainfall data of Colombo and Katugastota Sri Lanka. The results show that the ENN-GA model is more accurate than Bagging and Boosting models. The best performance for Colombo was obtained by ENN-GA with 14 members with RMSE 7.33 and for Katugastota by ENN-GA with 12 members with RMSE 6.25.