A clonal selection algorithm model for daily rainfall data prediction

作     者：Rodi, N. S. Noor Malek, M. A. Ismail, Amelia Ritahani Ting, Sie Chun Tang, Chao-Wei 

作者机构：Univ Tenaga Nas Dept Civil Engn Kajang 43000 Selangor Malaysia Int Islamic Univ Malaysia Kulliyyah Informat & Commun Technol Dept Comp Sci Kuala Lumpur 50728 Malaysia Cheng Shiu Univ Dept Civil Engn & Geomat Kaohsiung Taiwan 

出 版 物：《WATER SCIENCE AND TECHNOLOGY》 (水科学与技术)

年 卷 期：2014年第70卷第10期

页      面：1641-1647页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 08[工学] 0815[工学-水利工程] 

基　　金：Ministry of Higher Education Malaysia [FRGS/1-2012/(TK03)/(UNITEN)/(02/36)] 

主　　题：artificial immune system clonal selection algorithm daily rainfall prediction 

摘      要：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.