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

2017 ◽  
Vol 130 (3) ◽  
pp. 349-359 ◽  
Author(s):  
Özlem Baydaroğlu ◽  
Kasım Koçak ◽  
Kemal Duran
Keyword(s):  
Wavelet Transform ◽  
Support Vector Regression ◽  
Spectrum Analysis ◽  
River Flow ◽  
Singular Spectrum Analysis ◽  
Hybrid Models ◽  
Support Vector ◽  
Singular Spectrum ◽  
Flow Prediction

scholarly journals Linking Singular Spectrum Analysis and Machine Learning for Monthly Rainfall Forecasting

2020 ◽  
Vol 10 (9) ◽  
pp. 3224 ◽  
Author(s):  
Pa Ousman Bojang ◽  
Tao-Chang Yang ◽  
Quoc Bao Pham ◽  
Pao-Shan Yu
Keyword(s):  
Machine Learning ◽  
Water Resources ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Hybrid Models ◽  
Monthly Rainfall ◽  
Support Vector ◽  
Rainfall Forecasting ◽  
Singular Spectrum ◽  
Standard Models

Monthly rainfall forecasts can be translated into monthly runoff predictions that could support water resources planning and management activities. Therefore, development of monthly rainfall forecasting models in reservoir watersheds is essential for generating future rainfall amounts as an input to a water-resources-system simulation model to predict water shortage conditions. This research aims to examine the reliability of linking a data preprocessing method (singular spectrum analysis, SSA) with machine learning, least-squares support vector regression (LS-SVR), and random forest (RF), for monthly rainfall forecasting in two reservoir watersheds (Deji and Shihmen reservoir watersheds) located in Taiwan. Merging SSA with LS-SVR and RF, the hybrid models (SSA-LSSVR and SSA-RF) were developed and compared with the standard models (LS-SVR and RF). The proposed models were calibrated and validated using the watersheds’ observed areal monthly rainfalls separated into 70 percent of data for calibration and 30 percent of data for validation. Model performances were evaluated using two accuracy measures, root mean square error (RMSE) and Nash–Sutcliffe efficiency (NSE). Results show that the hybrid models could efficiently forecast monthly rainfalls. Nonetheless, the performances of the hybrid models vary in both watersheds which suggests that prior knowledge about the watershed’s hydrological behavior would be helpful to implement the appropriate model. Overall, the hybrid models significantly surpass the standard models for the two studied watersheds, which indicates that the proposed models are a prudent modeling approach that could be employed in the current research regions for monthly rainfall forecasting.

scholarly journals A hybrid model coupled with singular spectrum analysis for daily rainfall prediction

2010 ◽  
Vol 12 (4) ◽  
pp. 458-473 ◽  
Author(s):  
K. W. Chau ◽  
C. L. Wu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Regression ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Daily Rainfall ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Singular Spectrum ◽  
Artificial Neural

A hybrid model integrating artificial neural networks and support vector regression was developed for daily rainfall prediction. In the modeling process, singular spectrum analysis was first adopted to decompose the raw rainfall data. Fuzzy C-means clustering was then used to split the training set into three crisp subsets which may be associated with low-, medium- and high-intensity rainfall. Two local artificial neural network models were involved in training and predicting low- and medium-intensity subsets whereas a local support vector regression model was applied to the high-intensity subset. A conventional artificial neural network model was selected as the benchmark. The artificial neural network with the singular spectrum analysis was developed for the purpose of examining the singular spectrum analysis technique. The models were applied to two daily rainfall series from China at 1-day-, 2-day- and 3-day-ahead forecasting horizons. Results showed that the hybrid support vector regression model performed the best. The singular spectrum analysis model also exhibited considerable accuracy in rainfall forecasting. Also, two methods to filter reconstructed components of singular spectrum analysis, supervised and unsupervised approaches, were compared. The unsupervised method appeared more effective where nonlinear dependence between model inputs and output can be considered.

scholarly journals Short-Term Traffic Flow Prediction of Expressway: A Hybrid Method Based on Singular Spectrum Analysis Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chunyan Shuai ◽  
Zhengyang Pan ◽  
Lun Gao ◽  
HongWu Zuo
Keyword(s):  
Traffic Flow ◽  
Hybrid Model ◽  
Spectrum Analysis ◽  
Short Term Memory ◽  
Singular Spectrum Analysis ◽  
Support Vector ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Singular Spectrum ◽  
Flow Prediction

Real-time expressway traffic flow prediction is always an important research field of intelligent transportation, which is conducive to inducing and managing traffic flow in case of congestion. According to the characteristics of the traffic flow, this paper proposes a hybrid model, SSA-LSTM-SVR, to improve forecasting accuracy of the short-term traffic flow. Singular Spectrum Analysis (SSA) decomposes the traffic flow into one principle component and three random components, and then in terms of different characteristics of these components, Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) are applied to make prediction of different components, respectively. By fusing respective forecast results, SSA-LSTM-SVR obtains the final short-term predictive value. Experiments on the traffic flows of Guizhou expressway in January 2016 show that the proposed SSA-LSTM-SVR model has lower predictive errors and a higher accuracy and fitting goodness than other baselines. This illustrates that a hybrid model for traffic flow prediction based on components decomposition is more effective than a single model, since it can capture the main regularity and random variations of traffic flow.

scholarly journals An innovated integrated model using Singular Spectrum Analysis and Support Vector Regression optimized by intelligent algorithm for rainfall forecasting

2019 ◽  
Vol 2 (1) ◽  
pp. 46
Author(s):  
Weide Li ◽  
Juan Zhang
Keyword(s):  
Support Vector Regression ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Support Vector ◽  
Monthly Precipitation ◽  
Rainfall Forecasting ◽  
Dragonfly Algorithm ◽  
Singular Spectrum ◽  
Droughts And Floods ◽  
Precipitation Anomalies

Rainfall forecasting is becoming more and more significant and precipitation anomalies would lead to droughts and floods disasters. However, because of the complexity and non-stationary of rainfall data, it is difficult to forecast. In this paper, a novel hybrid model to forecast rainfall is developed by incorporating singular spectrum analysis (SSA) and dragonfly algorithm (DA) into support vector regression (SVR) method. Firstly, SSA is used for extracting the trend components of the hydrological data. Then, SVR is utilized to deal with the volatility and irregularity of the precipitation series. Finally, the parameter of SVR is optimized by DA. The proposed SSA-DA-SVR method is used to forecast the monthly precipitation for Songbai, Panshui, Lanma and Jiulongchi stations. To validate the efficiency of the method, four compared models, DA-SVR, SSA-GWO-SVR, SSA-PSO-SVR, SSA-CS-SVR are established. The result shows the proposed method has the best performance among all five models, and its prediction has high precision and accuracy.

scholarly journals Artifact Removal Methods in EEG Recordings: A Review

2021 ◽  
Vol 20 ◽  
pp. 35-56
Author(s):  
Mariyadasu Mathe ◽  
Padmaja Mididoddi ◽  
Battula Tirumala Krishna
Keyword(s):  
Wavelet Transform ◽  
Spectrum Analysis ◽  
Hybrid Methods ◽  
Singular Spectrum Analysis ◽  
Discrete Wavelet ◽  
Eeg Signals ◽  
Artifact Removal ◽  
Filtering Method ◽  
Advantages And Disadvantages ◽  
Singular Spectrum

To obtain the correct analysis of electroencephalogram (EEG) signals, non-physiological and physiological artifacts should be removed from EEG signals. This study aims to give an overview on the existing methodology for removing physiological artifacts, e.g., ocular, cardiac, and muscle artifacts. The datasets, simulation platforms, and performance measures of artifact removal methods in previous related research are summarized. The advantages and disadvantages of each technique are discussed, including regression method, filtering method, blind source separation (BSS), wavelet transform (WT), empirical mode decomposition (EMD), singular spectrum analysis (SSA), and independent vector analysis (IVA). Also, the applications of hybrid approaches are presented, including discrete wavelet transform - adaptive filtering method (DWT-AFM), DWT-BSS, EMD-BSS, singular spectrum analysis - adaptive noise canceler (SSA-ANC), SSA-BSS, and EMD-IVA. Finally, a comparative analysis for these existing methods is provided based on their performance and merits. The result shows that hybrid methods can remove the artifacts more effectively than individual methods.

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