A clonal selection algorithm model for daily rainfall data prediction

N. S. Noor Rodi, Marlinda Abdul Malek, Amelia Ritahani Ismail, Sie Chun Ting, Chao Wei Tang

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)1641-1647
Number of pages7
JournalWater Science and Technology
Volume70
Issue number10
DOIs
Publication statusPublished - 01 Jan 2014

Fingerprint

Rain
Immune system
rainfall
immune system
prediction
Hydrology
Time series
artificial neural network
hydrology
Neural networks
time series
Testing
method

All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Water Science and Technology

Cite this

Noor Rodi, N. S. ; Abdul Malek, Marlinda ; Ismail, Amelia Ritahani ; Ting, Sie Chun ; Tang, Chao Wei. / A clonal selection algorithm model for daily rainfall data prediction. In: Water Science and Technology. 2014 ; Vol. 70, No. 10. pp. 1641-1647.
@article{22176ca2f5e34a409a730c73c778081c,
title = "A clonal selection algorithm model for daily rainfall data prediction",
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.",
author = "{Noor Rodi}, {N. S.} and {Abdul Malek}, Marlinda and Ismail, {Amelia Ritahani} and Ting, {Sie Chun} and Tang, {Chao Wei}",
year = "2014",
month = "1",
day = "1",
doi = "10.2166/wst.2014.420",
language = "English",
volume = "70",
pages = "1641--1647",
journal = "Water Science and Technology",
issn = "0273-1223",
publisher = "IWA Publishing",
number = "10",

}

A clonal selection algorithm model for daily rainfall data prediction. / Noor Rodi, N. S.; Abdul Malek, Marlinda; Ismail, Amelia Ritahani; Ting, Sie Chun; Tang, Chao Wei.

In: Water Science and Technology, Vol. 70, No. 10, 01.01.2014, p. 1641-1647.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A clonal selection algorithm model for daily rainfall data prediction

AU - Noor Rodi, N. S.

AU - Abdul Malek, Marlinda

AU - Ismail, Amelia Ritahani

AU - Ting, Sie Chun

AU - Tang, Chao Wei

PY - 2014/1/1

Y1 - 2014/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84918792538&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84918792538&partnerID=8YFLogxK

U2 - 10.2166/wst.2014.420

DO - 10.2166/wst.2014.420

M3 - Article

C2 - 25429452

AN - SCOPUS:84918792538

VL - 70

SP - 1641

EP - 1647

JO - Water Science and Technology

JF - Water Science and Technology

SN - 0273-1223

IS - 10

ER -