Mohd Safar, Noor Zuraidin and Ndzi, David and Mahdin, Hairulnizam and Ku Khalif, Ku Muhammad Naim (2020) Rainfall intensity forecast using ensemble artificial neural network and data fusion for tropical climate. In: Fourth International Conference on Soft Computing and Data Mining (SCDM 2020), 22-23 January 2020, Melaka, Malaysia. (Submitted)
![[img]](http://eprints.uthm.edu.my/style/images/fileicons/text.png) |
Text
KP 2020 (71).pdf Restricted to Registered users only Download (895kB) | Request a copy |
Abstract
This paper proposes an ensemble method based on neural network architecture and stacking generalization. The objective is to develop a novel ensemble of Artificial Neural Network models with back propagation network and dynamic Recurrent Neural Network to improve prediction accuracy. Historical meteorological parameters and rainfall intensity have been used for predicting the rainfall intensity forecast. Hourly predicted rainfall intensity forecast are compared and analyzed for all models. The result shows that for 1 h of prediction, the neural network ensemble forecast model returns 94% of precision value. The study achieves that the ensemble neural network model shows significant improvement in prediction performance as compared to the individual neural network model.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | Rainfall forecasting ;artificial neural network; recurrent neural Network; expert system; ensemble learning; tropical climate |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television |
| Divisions: | Faculty of Computer Science and Information Technology > Department of Information Security |
| Depositing User: | Mrs. Normardiana Mardi |
| Date Deposited: | 02 Nov 2021 03:18 |
| Last Modified: | 02 Nov 2021 03:18 |
| URI: | http://eprints.uthm.edu.my/id/eprint/3421 |
Actions (login required)
 |
View Item |