Noor Rodi, Nur Syazwani and Abdul Malek, Marlinda and Zaini, Nur’Atiah A. and Ismail, Amelia Ritahani and M. Hisham, Mohd Hizwan (2020) Prediction of monthly rainfall at Senai, Johor using artificial immune system and deep learning neural network. Test Engineering and Management, 83. pp. 11740-11746. ISSN 0193-4120
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Abstract
In order to obtain good accuracy for the prediction of rainfall, this paper developed the Clonal Selection Algorithm (CSA) as a model for monthly rainfall prediction at Senai, Johor, Malaysia. CSA is one of the main algorithms in the Artificial Immune System. The results were compared with an established model for prediction which is the deep Multilayer Perceptron (MLP) algorithm. MLP is a deep learning algorithm used in the Artificial Neural Network (ANN). The algorithms were modelled using rainfall historical data with four input meteorological variables which are humidity, wind speed, pressure and temperature over the period of 1987 to 2017. The result shows that CSA obtained better prediction accuracy compared to MLP. CSA was applied successfully for the prediction of a continuous time series data with a high variable in nature.
| Item Type: | Article (Journal) |
|---|---|
| Additional Information: | 4296/82236 |
| Uncontrolled Keywords: | Continuous time systems, Deep neural networks, Forecasting, Immune system, Learning algorithms, Neural networks, Rain, Wind |
| Subjects: | Q Science > Q Science (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering |
| Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): | Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology |
| Depositing User: | Amelia Ritahani Ismail |
| Date Deposited: | 17 Aug 2020 16:36 |
| Last Modified: | 17 Aug 2020 16:36 |
| URI: | http://irep.iium.edu.my/id/eprint/82236 |
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