@inproceedings{2d4e52a85b7844efb269dfd0e1f69161,
title = "Rainfall intensity forecast using ensemble artificial neural network and data fusion for tropical climate",
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.",
keywords = "Artificial Neural Network, ensemble learning, expert system, rainfall forecasting, recurrent neural network, tropical climate",
author = "{Mohd Safar}, {Noor Zuraidin} and David Ndzi and Hairulnizam Mahdin and Khalif, {Ku Muhammad Naim Ku}",
year = "2020",
month = jan,
day = "22",
doi = "10.1007/978-3-030-36056-6_24",
language = "English",
isbn = "9783030360559",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer",
pages = "241--250",
editor = "Rozaida Ghazali and Nawi, {Nazri Mohd} and Deris, {Mustafa Mat} and Abawajy, {Jemal H.}",
booktitle = "Recent Advances on Soft Computing and Data Mining - Proceedings of the 4th International Conference on Soft Computing and Data Mining, SCDM 2020",
note = "4th International Conference on Soft Computing and Data Mining, SCDM 2020 ; Conference date: 22-01-2020 Through 23-01-2020",
}