Effect of Dimensionality Reductions Technique in Modelling and Forecasting River Flow
Keywords:Dimensionality Reduction, Forecasting, River Flow, Least Square Support Vector Machine, Principal Component Analysis
The ability of obtain accurate information on future river flow is a fundamental key for water resources planning, and management. Traditionally, single models have been introduced to predict the future value of river flow. This paper investigates the ability of Principal Component Analysis as dimensionality reduction technique and combined with single Support Vector Machine and Least Square Support Vector Machine, referred to as PCA-SVM and PCA-LSSVM. This study also presents comparison between the proposed models with single models of SVM and LSSVM. These models are ranked based on four statistical measures namely Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Correlation Coefficient ( ), and Correlation of Efficiency (CE). The results shows that PCA combined with LSSVM has better performance compared to other models. The best ranked models are then measured using Mean of Forecasting Error (MFE) to determine its forecast rate. PCA-LSSVM proven to be better model as it also indicates a small percentage of under-predicted values compared to the observed river flow values of 0.89% for Tualang river while over-predicted by 2. 08% for Bernam river. The study concludes by recommending the PCA as dimension reduction approach combined with LSSVM for river flow forecasting due to better prediction results and stability than those achieved from single models
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