A clonal selection algorithm model for daily rainfall data prediction

2014 ◽  
Vol 70 (10) ◽  
pp. 1641-1647
Author(s):  
N. S. Noor Rodi ◽  
M. A. Malek ◽  
Amelia Ritahani Ismail ◽  
Sie Chun Ting ◽  
Chao-Wei Tang
Keyword(s):  
Clonal Selection ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Series Data ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Immune Systems ◽  
Daily Rainfall Data ◽  
Data Prediction ◽  
Testing Stage

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.

scholarly journals Monthly Rainfall Prediction Model of Peninsular Malaysia Using Clonal Selection Algorithm

2018 ◽  
Vol 7 (4.35) ◽  
pp. 182
Author(s):  
N.S.Noor Rodi ◽  
M.A. Malek ◽  
A.R. Ismail
Keyword(s):  
Immune System ◽  
Clonal Selection ◽  
Peninsular Malaysia ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Series Data ◽  
Monthly Interval ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Rainfall Prediction

Nowadays, various algorithms inspired by natural processes have been extensively applied in solving engineering problems. This study proposed Artificial Immune Systems (AIS), a computational approach inspired by the processes of human immune system, as an algorithm to predict future rainfall. This proposed algorithm is another alternative technique as compared to the commonly used Statistical, Stochastic and Artificial Neural Network techniques traditionally use in Hydrology. Rainfall prediction is pertinent in order to solve many hydrological problems. The proposed Clonal Selection Algorithm (CSA) is one of the main algorithms in AIS, which inspired on Clonal selection theory in the immune system of human body that includes selection, hyper mutation, and receptor editing processes. This study proposed algorithm is utilised to predict future monthly rainfall in Peninsular Malaysia. The collected data includes rainfall and other four (4) meteorological parameters from year 1988 to 2017 at four selected meteorological stations. The parameters used in this analysis are humidity, wind speed, temperature and pressure at monthly interval.  Four (4) meteorological stations involved are Chuping (north), Subang Jaya(west), Senai (south) and Kota Bharu (west) represented peninsular Malaysia. Based on the results at testing stage, it is found that the trend and peaks of the hydrographs from generated data are approximately similar to the actual historical data. The highest similarity percentage obtained is 91%. The high values of similarity percentage obtained between simulated and actual rainfall data in this study, reinforced the hypothesis that CSA is suitable to be used for prediction of continuous time series data such as monthly rainfall data which highly variable in nature.  As a conclusion, the results showed that the proposed Clonal Selection Algorithm is acceptable and stable at all stations.

Immune-Inspired Quantum Genetic Optimization Algorithm and its Application

2010 ◽  
Vol 143-144 ◽  
pp. 547-551
Author(s):  
Zhe Lian
Keyword(s):  
Optimization Algorithm ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Computation Method ◽  
Clonal Selection Algorithm ◽  
Genetic Optimization ◽  
Selection Algorithm ◽  
Human Immune System ◽  
Immune Systems ◽  
Genetic Optimization Algorithm

Artificial immune systems (AIS), inspired by the natural immune systems, are an emerging kind of soft computing methods. This paper brings forward an immune-inspired quantum genetic optimization algorithm (IQGOA) based on clonal selection algorithm. The IQGOA is an evolutionary computation method inspired by the immune clonal principle of human immune system. To show the versatility and flexibility of the proposed IQGOA, some examples are given. Experimental results have shown that IQGOA is superior to clonal selection algorithm and Genetic Algorithm (GA) on performance.

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