Rainfall intensity forecast using ensemble artificial neural network and data fusion for tropical climate
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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2020
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/3421/1/KP%202020%20%2871%29.pdf http://eprints.uthm.edu.my/3421/ https://doi.org/10.1007/978-3-030-36056-6_24 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Tun Hussein Onn Malaysia |
| Language: | English |
| Summary: | 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. |
|---|