Please use this identifier to cite or link to this item:
PIRA download icon_1.1View/Download Full Text
Title: Prediction of rainfall time series using modular soft computing methods
Authors: Wu, CL
Chau, KW 
Issue Date: Mar-2013
Source: Engineering applications of artificial intelligence, Mar. 2013, v. 26, no. 3, p. 997–1007
Abstract: In this paper, several soft computing approaches were employed for rainfall prediction. Two aspects were considered to improve the accuracy of rainfall prediction: (1) carrying out a data-preprocessing procedure and (2) adopting a modular modeling method. The proposed preprocessing techniques included moving average (MA) and singular spectrum analysis (SSA). The modular models were composed of local support vectors regression (SVR) models or/and local artificial neural networks (ANN) models. In the process of rainfall forecasting, the ANN was first used to choose data-preprocessing method from MA and SSA. Modular models involved preprocessing the training data into three crisp subsets (low, medium and high levels) according to the magnitudes of the training data, and finally two SVRs were performed in the medium and high-level subsets whereas ANN or SVR was involved in training and predicting the low-level subset. For daily rainfall record, the low-level subset tended to be modeled by the ANN because it was overwhelming in the training data, which is based on the fact that the ANN is very efficient in training large-size samples due to its parallel information processing configuration. Four rainfall time series consisting of two monthly rainfalls and two daily rainfalls from different regions were utilized to evaluate modular models at 1-day, 2-day, and 3-day lead-time with the persistence method and the global ANN as benchmarks. Results showed that the MA was superior to the SSA when they were coupled with the ANN. Comparison results indicated that modular models (referred to as ANN-SVR for daily rainfall simulations and MSVR for monthly rainfall simulations) outperformed other models. The ANN-MA also displayed considerable accuracy in rainfall forecasts compared with the benchmark.
Keywords: Rainfall prediction
Moving average
Singular spectral analysis
Modular model
Publisher: Pergamon Press
Journal: Engineering applications of artificial intelligence 
ISSN: 0952-1976
EISSN: 1873-6769
DOI: 10.1016/j.engappai.2012.05.023
Rights: © 2012 Elsevier Ltd. All rights reserved.
NOTICE: this is the author’s version of a work that was accepted for publication in Engineering Applications of Artificial Intelligence. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Engineering Applications of Artificial Intelligence, vol 26, issue 3, March 2013 DOI:10.1016/j.engappai.2012.05.023
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
EAAI8.pdfPre-published version407.43 kBAdobe PDFView/Open
Open Access Information
Status open access
File Version Final Accepted Manuscript
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

Last Week
Last month
Citations as of Jun 4, 2023


Citations as of Jun 4, 2023


Last Week
Last month
Citations as of Jun 8, 2023


Last Week
Last month
Citations as of Jun 8, 2023

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.