Integration of GWO-LSSVM for time series predictive analysis
The emergence of Statistical Learning Theory (SLT) based algorithm namely Least Squares Support Vector Machines (LSSVM) has evidenced its efficacy in solving regression and classification problems. In this study, LSSVM is employed as a predictor to predict water level. Accurate water levels model...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IET
2016
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/21490/ http://umpir.ump.edu.my/id/eprint/21490/ http://umpir.ump.edu.my/id/eprint/21490/1/Integration%20Of%20GWO-LSSVM%20For%20Time%20Series%20Predictive%20Analysis.pdf |
| Summary: | The emergence of Statistical Learning Theory (SLT) based algorithm namely Least Squares Support Vector Machines (LSSVM) has evidenced its efficacy in solving regression and classification problems. In this study, LSSVM is employed as a predictor to predict water level. Accurate water levels modeling and prediction is vital for safety especially during the monsoon season. It is worth noting that, mitigating the effects of floods can be accomplished by using either structural or non-structural measures, or by a combination of both. Structural measures incorporated of engineering works, such as channelization or flood reservoirs which changes the shape of the flood hydrograph. On the other hand, non-structural measures such as flood warning scheme is intended to reduce the economic losses in a flood
situation. In most cases, disaster prevention operation such flood warning scheme proved to be more efficient in mitigating the effects of major floods than structural measures. Thus, for this study, a hybrid algorithm of LSSVM with one of the recent bio-inspired optimization algorithm, namely Grey Wolf Optimizer (GWO-LSSVM) is presented for water level prediction. The GWO is utilized to optimize the parameters of LSSVM. The feasibility of GWO-LSSVM is compared against LSSVM optimized by Firefly Algorithm (FA-LSSVM) and single GWO. Findings of the study demonstrated that the GWO-LSSVM able to provide competitive results and able to perform well for the problem under study. |
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