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Title: A clonal selection algorithm model for daily rainfall data prediction
Authors: Noor Rodi, N.S. 
Malek, M.A. 
Ismail, A.R. 
Ting, S.C. 
Tang, C.-W. 
Issue Date: 2014
Abstract: This study applies the clonal selection algorithm (CSA) in an artificial immune system (AIS) as an alternative method to predicting future rainfall data. The stochastic and the artificial neural network techniques are commonly used in hydrology. However, in this study a novel technique for forecasting rainfall was established. Results from this study have proven that the theory of biological immune systems could be technically applied to time series data. Biological immune systems are nonlinear and chaotic in nature similar to the daily rainfall data. This study discovered that the proposed CSA was able to predict the daily rainfall data with an accuracy of 90% during the model training stage. In the testing stage, the results showed that an accuracy between the actual and the generated data was within the range of 75 to 92%. Thus, the CSA approach shows a new method in rainfall data prediction. © IWA Publishing 2014.
Appears in Collections:COE Scholarly Publication

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