River flow prediction using hybrid models of support vector regression with the wavelet transform, singular spectrum analysis and chaotic approach

Baydaroğlu, Özlem; Koçak, Kasım; Duran, Kemal

doi:10.1007/s00703-017-0518-9

Cited by 29 publications

(14 citation statements)

References 53 publications

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“…Support Vector Regression (SVR) originates from Support Vector Machines (SVM), originally introduced by Vapnik [30]. Unlike a SVM classifier, instead of being used for binary classification, SVR aims to estimate a continuous value as output [31]. The formula is stated as…”

Section: E Regression Analysismentioning

confidence: 99%

Regression-Based Continuous Driving Fatigue Estimation: Toward Practical Implementation

Bose

Wang

Dragomir

et al. 2020

IEEE Trans. Cogn. Dev. Syst.

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Mental fatigue in drivers is one of the leading causes that give rise to traffic accidents. Electroencephalography (EEG) based driving fatigue studies showed promising performance in fatigue monitoring. However, complex methodologies are not suitable for practical implementation. In our simulation based setup that retained the constraints of real driving, we took a step closer to fatigue estimation in a practical scenario. We adopted a pre-processing pipeline with low computational complexity, which can be easily and practically implemented in real-time. Moreover, regression-based continuous fatigue estimation was achieved using power spectral features in conjunction with time as the fatigue label. We sought to compare three regression models and three time windows to demonstrate their effects on the performance of fatigue estimation. Dynamic time warping was proposed as a new measure for evaluating the performance of fatigue estimation. The results derived from the validation of the proposed framework on 19 subjects showed that our proposed framework was promising towards practical implementation. Fatigue estimation by the support vector regression with radial basis function kernel and 5-second window length achieved the best performance. We also provided a comprehensive analysis on the spatial distribution of channels and frequency bands mostly contributing to fatigue estimation, which can inform the feature and channel reduction for real-time fatigue monitoring in practical driving. After reducing the number of electrodes by 75%, the proposed framework retained comparable performance in fatigue estimation. This study demonstrates the feasibility and adaptability of our proposed framework in practical implementation of mental fatigue estimation.

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Section: E Regression Analysismentioning

confidence: 99%

Regression-Based Continuous Driving Fatigue Estimation: Toward Practical Implementation

Bose

Wang

Dragomir

et al. 2020

IEEE Trans. Cogn. Dev. Syst.

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“…It has been reported that these hybrid MLMs, which consists of time series decomposition and sub-time series modeling, were able to achieve better performance compared with the single MLMs. Finally, the hybrid MLMs, combined with more than two methods, have been developed for rainfall-runoff and streamflow modeling including DWT, PSO, and SVMs [45]; DWT, GA, and adaptive neuro-fuzzy inference system (ANFIS) [46]; EEMD, PSO, and SVMs [47]; EEMD, SOM, and linear genetic programming [48]; wavelet transform, singular spectrum, chaotic approach, and SVR [49]; and Hermite-projection pursuit regression, social spider optimization, and least square algorithm [50].…”

Section: Introductionmentioning

confidence: 99%

“…They revealed that the hybrid model provided a better alternative compared with the SVMs for monthly streamflow forecasting. Baydaroglu et al [49] presented a coupling model of WT, chaotic approach (CA), singular spectrum analysis (SSA) and SVR. They proved that WT, SSA and CA for configuring the input matrix of the SVR were effective in the hybrid modeling for river flow prediction.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

2018

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Accurate modeling for nonlinear and nonstationary rainfall-runoff processes is essential for performing hydrologic practices effectively. This paper proposes two hybrid machine learning models (MLMs) coupled with variational mode decomposition (VMD) to enhance the accuracy for daily rainfall-runoff modeling. These hybrid MLMs consist of VMD-based extreme learning machine (VMD-ELM) and VMD-based least squares support vector regression (VMD-LSSVR). The VMD is employed to decompose original input and target time series into sub-time series called intrinsic mode functions (IMFs). The ELM and LSSVR models are selected for developing daily rainfall-runoff models utilizing the IMFs as inputs. The performances of VMD-ELM and VMD-LSSVR models are evaluated utilizing efficiency and effectiveness indices. Their performances are also compared with those of VMD-based artificial neural network (VMD-ANN), discrete wavelet transform (DWT)-based MLMs (DWT-ELM, DWT-LSSVR, and DWT-ANN) and single MLMs (ELM, LSSVR, and ANN). As a result, the VMD-based MLMs provide better accuracy compared with the single MLMs and yield slightly better performance than the DWT-based MLMs. Among all models, the VMD-ELM and VMD-LSSVR models achieve the best performance in daily rainfall-runoff modeling with respect to efficiency and effectiveness. Therefore, the VMD-ELM and VMD-LSSVR models can be an alternative tool for reliable and accurate daily rainfall-runoff modeling.

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“…River flow prediction has emerged from hydrological modeling and transformed into a dynamic and active research area [2,3]. Studying the river flow and stream flow is fundamental to flood protection, sustainable irrigation, and urban development [4,5]. Due to the uncertainties in atmospheric behavior associated with change climate, the dynamic and data-driven methods for hydrological modeling of catchments have become popular more than ever [6].…”

Section: Introductionmentioning

confidence: 99%

Support Vector Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin

Samadianfard

Jarhan

Salwana

et al. 2019

Water

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Advancement in river flow prediction systems can greatly empower the operational river management to make better decisions, practices, and policies. Machine learning methods recently have shown promising results in building accurate models for river flow prediction. This paper aims to identify models with higher accuracy, robustness, and generalization ability by inspecting the accuracy of a number of machine learning models. The proposed models for river flow include support vector regression (SVR), a hybrid of SVR with a fruit fly optimization algorithm (FOA) (so-called FOASVR), and an M5 model tree (M5). Additionally, the influence of periodicity (π) on the forecasting enactment was examined. To assess the performance of the proposed models, different statistical meters were implemented, including root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R), and Bayesian information criterion (BIC). Results showed that the FOASVR with RMSE (4.36 and 6.33 m 3 /s), MAE (2.40 and 3.71 m 3 /s) and R (0.82 and 0.81) values had the best performance in forecasting river flows at Babarud and Vaniar stations, respectively. Also, regarding BIC parameters, Q t−1 and π were selected as parsimonious inputs for predicting river flow one month ahead. Overall findings indicated that, although both the FOASVR and M5 predicted the river flows in suitable accordance with observed river flows, the performance of the FOASVR was moderately better than the M5 and periodicity noticeably increased the performance of the models; consequently, FOASVR can be suggested as the most accurate method for forecasting river flows.

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River flow prediction using hybrid models of support vector regression with the wavelet transform, singular spectrum analysis and chaotic approach

Cited by 29 publications

References 53 publications

Regression-Based Continuous Driving Fatigue Estimation: Toward Practical Implementation

Regression-Based Continuous Driving Fatigue Estimation: Toward Practical Implementation

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

Support Vector Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin

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