Short term forecasting based on hybrid least squares support vector machines

Zuriani, Mustaffa and M. H., Sulaiman and Ernawan, Ferda and Noorhuzaimi, Mohd Noor (2018) Short term forecasting based on hybrid least squares support vector machines. Advanced Science Letters, 24 (10). pp. 7455-7460. ISSN 1936-6612. (Published)

29. Short Term Forecasting based on Hybrid Least Squares Support Vector Machines1.pdf

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Flood is one of the common natural disasters that have caused universal damage throughout the world. Due to that matter, reliable flood forecasting is crucial for the purpose of preventing loss of life and minimizing property damage. In this study, hybrid Least Squares Support Vector Machines (LSSVM) with four meta-heuristic algorithms viz. Grey Wolf Optimizer (GWO-LSSVM), Cuckoo Search (CS-LSSVM), Genetic Algorithm (GA-LSSVM) and Differential Evolution (DE-LSSVM) are presented for a week ahead water level forecasting. The employed meta-heuristic algorithms are individually served as an optimization tool for LSSVM and later, the forecasting is proceeded by LSSVM. This study assesses the performance of each hybrid algorithms based on three statistical indices viz. Mean Square Error (MSE), Root Mean Square Percentage Error (RMSPE) and Theil’s U which is realized on raw and normalized data set. Later, the performance of each identified hybrid algorithm is analyzed and discussed. From the simulations, it is demonstrated that all the identified algorithms are able to produce better forecasting result by using normalized time series data.

Item Type: Article
Additional Information: JCR® Category: Multidisciplinary Sciences. Quartile: Q2
Uncontrolled Keywords: Computational Intelligence; Flood forecasting; Least Squares Support Vector Machines; Meta-heuristic algorithm; Optimization.
Subjects: Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Mrs. Neng Sury Sulaiman
Date Deposited: 06 Mar 2018 03:05
Last Modified: 13 Nov 2018 01:45
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