River Stage Modeling by Combining Maximal Overlap Discrete Wavelet Transform, Support Vector Machines and Genetic Algorithm

Abstract

This paper proposes a river stage modeling approach combining maximal overlap discrete wavelet transform (MODWT), support vector machines (SVMs) and genetic algorithm (GA). The MODWT decomposes original river stage time series into sub-time series (detail and approximation components). The SVM computes daily river stage values using the decomposed sub-time series. The GA searches for the optimal hyperparameters of SVM. The performance of MODWT-SVM models is evaluated using efficiency and effectiveness indices; and compared with that of a single model (multilayer perceptron (MLP) and SVM), discrete wavelet transform (DWT)-based models (DWT-MLP and DWT-SVM) and MODWT-MLP models. The conjunction of MODWT, SVM and GA improves the performance of the SVM model and outperforms the single models. The MODWT-based models using the SVM model enhance model performance and accuracy compared to those of using MLP model. Also, hybrid models coupling MODWT, SVM and GA improve model performance and accuracy in daily river stage modeling as compared with those combined with DWT. The MODWT-SVM model using the Coiflet 12 (c12) mother wavelet, MODWT-SVM-c12, produces the best efficiency and effectiveness among all models. Therefore, the conjunction of MODWT, SVM and GA can be an efficient and effective approach for modeling daily river stages.

Electrical Energy Demand Forecasting Model Development and Evaluation with Maximum Overlap Discrete Wavelet Transform-Online Sequential Extreme Learning Machines Algorithms

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“…and Symlets (sym i , i = 2, 3, • • • , 10). The maximum level of decomposition (J) was computed using Equation ( 6) [6,17,28]:…”

Multi-temporal analysis for drought classifying based on SPEI gridded data and hybrid maximal overlap discrete wavelet transform

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Rainfall prediction optimization model in ten-day time step based on sliding window mechanism and zero sum game

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