scholarly journals Wavelet and cuckoo search-support vector machine conjugation for drought forecasting using Standardized Precipitation Index (case study: Urmia Lake, Iran)

2018 ◽  
Vol 20 (4) ◽  
pp. 975-988 ◽  
Author(s):  
Mehdi Komasi ◽  
Soroush Sharghi ◽  
Hamid R. Safavi
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Standardized Precipitation Index ◽  
Cuckoo Search ◽  
Precipitation Index ◽  
Urmia Lake ◽  
Support Vector ◽  
Drought Forecasting ◽  
Svm Algorithm ◽  
Svm Model

Abstract In this study, wavelet-support vector machine (WSVM) is proposed for drought forecasting using the Standardized Precipitation Index (SPI). In this way, the SPI time series of Urmia Lake watershed is decomposed to multiple frequency time series by wavelet transform. Then, these time sub-series are applied as input data to the support vector machine (SVM) model to forecast drought. Also, a cuckoo search (CS)-based approach is proposed for parameter optimization of SVM, finding the best initial constant parameters of the SVM algorithm. The obtained results indicate that the radial basis function (RBF)-kernel function of the SVM algorithm has high efficiency in the SPI modeling, resulting in a determination coefficient (DC) of 0.865 in verification step. In the WSVM model, the Coif1, which is considered as a mother wavelet function with decomposition level of five, shows a better performance with DC of 0.954 in verification step, revealing that the proposed hybrid WSVM model outperforms the single SVM model in forecasting SPI time series. Also, DC of cuckoo search-support vector machine (CS-SVM) is calculated to be 0.912 in verification step, indicating the fact that the proposed CS-SVM model shows better efficiency than single SVM model.

scholarly journals PSO-Based Support Vector Machine with Cuckoo Search Technique for Clinical Disease Diagnoses

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaoyong Liu ◽  
Hui Fu
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Cuckoo Search ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Svm Model ◽  
Two Stages ◽  
Best Parameters

Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM), particle swarm optimization (PSO), and cuckoo search (CS). The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and then PSO is applied to continue SVM training and find the best parameters of SVM. Experimental results indicate that the proposed CS-PSO-SVM model achieves better classification accuracy and F-measure than PSO-SVM and GA-SVM. Therefore, we can conclude that our proposed method is very efficient compared to the previously reported algorithms.

A Novel Hybrid Model for Solar Radiation Forecasting Using Support Vector Machine and Bee Colony Optimization Algorithm: Review and Case Study

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
Mawloud Guermoui ◽  
Kacem Gairaa ◽  
John Boland ◽  
Toufik Arrif
Keyword(s):  
Support Vector Machine ◽  
Solar Radiation ◽  
Least Squares ◽  
Correlation Coefficient ◽  
Cuckoo Search ◽  
Measured Data ◽  
Parameters Optimization ◽  
Support Vector ◽  
Bee Colony ◽  
Svm Model

Abstract This article proposes a new hybrid least squares-support vector machine and artificial bee colony algorithm (ABC-LS-SVM) for multi-hour ahead forecasting of global solar radiation (GHI) data. The framework performs on training the least squares-support vector machine (LS-SVM) model by means of the ABC algorithm using the measured data. ABC is developed for free parameters optimization for the LS-SVM model in a search space so as to boost the forecasting performance. The developed ABC-LS-SVM approach is verified on an hourly scale on a database of five years of measurements. The measured data were collected from 2013 to 2017 at the Applied Research Unit for Renewable Energy (URAER) in Ghardaia, south of Algeria. Several combinations of input data have been tested to model the desired output. Forecasting results of 12 h ahead GHI with the ABC-LS-SVM model led to the root-mean-square error (RMSE) equal to 116.22 Wh/m2, Correlation coefficient r = 94.3%. With the classical LS-SVM, the RMSE error equals to 117.73 Wh/m2 and correlation coefficient r = 92.42%; for cuckoo search algorithm combined with LS-SVM, the RMSE = 116.89 Wh/m2 and r = 93.78%. The results achieved reveal that the proposed hybridization scheme provides a more accurate performance compared to cuckoo search-LS-SVM and the stand-alone LS-SVM.

scholarly journals Comparison of Support Vector Machine-Based Techniques for Detection of Bearing Faults

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Lijun Wang ◽  
Shengfei Ji ◽  
Nanyang Ji
Keyword(s):  
Support Vector Machine ◽  
Global Optimization ◽  
Fault Diagnosis ◽  
Model Simulation ◽  
Rolling Bearing ◽  
Support Vector ◽  
Denoising Method ◽  
Svm Algorithm ◽  
Svm Model ◽  
Shuffled Frog Leaping

This paper presents a method that combines Shuffled Frog Leaping Algorithm (SFLA) with Support Vector Machine (SVM) method in order to identify the fault types of rolling bearing in the gearbox. The proposed method improves the accuracy of fault diagnosis identification after processing the collected vibration signals through wavelet threshold denoising. The global optimization and high computational efficiency of SFLA are applied to the SVM model. Simulation results show that the SFLA-SVM algorithm is effective in fault diagnosis. Compared with SVM and Particle Swarm Optimization SVM (PSO-SVM) algorithms, it is demonstrated that the SFLA-SVM algorithm has the advantages of better global optimization, higher accuracy, and better reliability of diagnosis. Its accuracy is further improved through the integration of the wavelet threshold denoising method.

Hybrid wavelet-support vector machine approach for modelling rainfall–runoff process

2016 ◽  
Vol 73 (8) ◽  
pp. 1937-1953 ◽  
Author(s):  
Mehdi Komasi ◽  
Soroush Sharghi
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Fuzzy Inference ◽  
The Other ◽  
Support Vector ◽  
Rainfall Runoff ◽  
Model Concept ◽  
Inference System ◽  
Svm Model ◽  
Artificial Intelligence Models

Because of the importance of water resources management, the need for accurate modeling of the rainfall–runoff process has rapidly grown in the past decades. Recently, the support vector machine (SVM) approach has been used by hydrologists for rainfall–runoff modeling and the other fields of hydrology. Similar to the other artificial intelligence models, such as artificial neural network (ANN) and adaptive neural fuzzy inference system, the SVM model is based on the autoregressive properties. In this paper, the wavelet analysis was linked to the SVM model concept for modeling the rainfall–runoff process of Aghchai and Eel River watersheds. In this way, the main time series of two variables, rainfall and runoff, were decomposed to multiple frequent time series by wavelet theory; then, these time series were imposed as input data on the SVM model in order to predict the runoff discharge one day ahead. The obtained results show that the wavelet SVM model can predict both short- and long-term runoff discharges by considering the seasonality effects. Also, the proposed hybrid model is relatively more appropriate than classical autoregressive ones such as ANN and SVM because it uses the multi-scale time series of rainfall and runoff data in the modeling process.

scholarly journals Predicting the Direction Movement of Financial Time Series Using Artificial Neural Network and Support Vector Machine

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Muhammad Ali ◽  
Dost Muhammad Khan ◽  
Muhammad Aamir ◽  
Amjad Ali ◽  
Zubair Ahmad
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Composite Index ◽  
Support Vector ◽  
Financial Time ◽  
Svm Model

Prediction of financial time series such as stock and stock indexes has remained the main focus of researchers because of its composite nature and instability in almost all of the developing and advanced countries. The main objective of this research work is to predict the direction movement of the daily stock prices index using the artificial neural network (ANN) and support vector machine (SVM). The datasets utilized in this study are the KSE-100 index of the Pakistan stock exchange, Korea composite stock price index (KOSPI), Nikkei 225 index of the Tokyo stock exchange, and Shenzhen stock exchange (SZSE) composite index for the last ten years that is from 2011 to 2020. To build the architect of a single layer ANN and SVM model with linear, radial basis function (RBF), and polynomial kernels, different technical indicators derived from the daily stock trading, such as closing, opening, daily high, and daily low prices and used as input layers. Since both the ANN and SVM models were used as classifiers; therefore, accuracy and F-score were used as performance metrics calculated from the confusion matrix. It can be concluded from the results that ANN performs better than SVM model in terms of accuracy and F-score to predict the direction movement of the KSE-100 index, KOSPI index, Nikkei 225 index, and SZSE composite index daily closing price movement.

scholarly journals Stability Evaluation of Rock Slope in Hydraulic Engineering Based on Improved Support Vector Machine Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fei Li ◽  
Hongyun Zhang
Keyword(s):  
Support Vector Machine ◽  
Stability Analysis ◽  
Permeability Coefficient ◽  
Engineering Geology ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Support Vector ◽  
Svm Algorithm ◽  
Improved Support Vector Machine

The safety problem of the slope has always been an important subject in engineering geology, which has a wide range of application background and practical significance in reality. How to correctly evaluate the stability of the slope and obtain the parameters of the slope has always been the focus of research and production personnel at home and abroad. In recent years, various artificial intelligence calculation methods have been applied to the field of rock engineering and engineering geology, providing some new ideas for the solution of slope stability analysis and parameter back analysis. Support vector machine (SVM) algorithm has unique advantages and generalization in dealing with finite samples and highly complex and nonlinear problems. At present, it has become a research hotspot of intelligent methods and has been widely paid attention to in various application fields of slope engineering. In this paper, a cuckoo search algorithm-improved support vector machine (CS-SVM) method is applied to slope stability analysis and parameter inversion. Aiming at the problem of selecting kernel function parameters and penalty number of SVM, a method of using cuckoo search algorithm to improve support vector machine was proposed, and the global optimization ability of cuckoo search algorithm was used to improve the algorithm. Aiming at the slope samples collected, the classification algorithm of support vector machine (SVM) was used to identify the stable state of the test samples, and the improved SVM algorithm was used to analyze the safety factor of the test samples. The results show that the proposed method is reasonable and reliable. Based on the inversion of the permeability coefficient of the test samples by the improved support vector machine, the comparison between the inversion value and the theoretical value shows that it is basically feasible to invert the permeability coefficient of the dam slope by the improved support vector machine.

scholarly journals A Conjunction Method of Wavelet Transform-Particle Swarm Optimization-Support Vector Machine for Streamflow Forecasting

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Fanping Zhang ◽  
Huichao Dai ◽  
Deshan Tang
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Wavelet Transform ◽  
Correlation Coefficients ◽  
Support Vector ◽  
Streamflow Forecasting ◽  
Swarm Optimization ◽  
Svm Model ◽  
Monthly Streamflow

Streamflow forecasting has an important role in water resource management and reservoir operation. Support vector machine (SVM) is an appropriate and suitable method for streamflow prediction due to its best versatility, robustness, and effectiveness. In this study, a wavelet transform particle swarm optimization support vector machine (WT-PSO-SVM) model is proposed and applied for streamflow time series prediction. Firstly, the streamflow time series were decomposed into various details (Ds) and an approximation (A3) at three resolution levels (21-22-23) using Daubechies (db3) discrete wavelet. Correlation coefficients between eachDsubtime series and original monthly streamflow time series are calculated.Dscomponents with high correlation coefficients (D3) are added to the approximation (A3) as the input values of the SVM model. Secondly, the PSO is employed to select the optimal parameters,C,ε, andσ, of the SVM model. Finally, the WT-PSO-SVM models are trained and tested by the monthly streamflow time series of Tangnaihai Station located in Yellow River upper stream from January 1956 to December 2008. The test results indicate that the WT-PSO-SVM approach provide a superior alternative to the single SVM model for forecasting monthly streamflow in situations without formulating models for internal structure of the watershed.

A New Time Series Regression Method Based on Support Vector Machine Plus and Genetic Algorithm

2011 ◽  
Vol 201-203 ◽  
pp. 2277-2280
Author(s):  
Wei Sun ◽  
Guo Xiang Meng ◽  
Qian Ye ◽  
Jian Zheng Zhang ◽  
Li Weng Zhang
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Support Vector Machine ◽  
Regression Method ◽  
Experimental Result ◽  
Support Vector ◽  
Hidden Information ◽  
Time Series Regression ◽  
Svm Model ◽  
New Time

Support vector machine (SVM) is gaining popularity on time series analysis due to its advanced theory foundation. The introduction of the hidden information on the basis of SVM is called support vector machine plus (SVM+). However, the hidden information which provides something closely associated with the time series increases the difficulty of training SVM model. In this paper, a new time series regression method GA-RSVM+ is put forward, in which Genetic Algorithm (GA) is used to search the optimal combination of free parameters. The experimental result shows that GA-RSVM+ can accurately determine the parameters on its own and achieve best regression precision. This method has a clear advantage in the regression analysis of time series.

scholarly journals Rapid Identification of Nine Easily Confused Mineral Traditional Chinese Medicines Using Raman Spectroscopy Based on Support Vector Machine

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Jing Ming ◽  
Long Chen ◽  
Yan Cao ◽  
Chi Yu ◽  
Bi-Sheng Huang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Raman Spectroscopy ◽  
Rapid Identification ◽  
Morphological Properties ◽  
Support Vector ◽  
Traditional Chinese Medicines ◽  
Intensity Data ◽  
Chinese Medicines ◽  
Svm Algorithm ◽  
Svm Model

Mineral traditional Chinese medicines (TCMs) are natural minerals, mineral processing products, and some fossils of animals or animal bones that can be used as medicines. Mineral TCMs are a characteristic part of TCMs and play a unique role in the development of TCMs. Mineral TCMs are usually identified according to their morphological properties such as shape, color, or smell, but it is difficult to separate TCMs that are similar in appearance or smell. In this study, the feasibility of using Raman spectroscopy combined with support vector machine (SVM) for rapid identification of nine easily confused mineral TCMs, i.e., borax, gypsum fibrosum, natrii sulfas exsiccatus, natrii sulfas, alumen, sal ammoniac, quartz, calcite, and yellow croaker otolith, was investigated. Initially, two methods, characteristic intensity data extraction and principal component analysis (PCA), were performed to reduce the dimensionality of spectral data. The identification model was subsequently built by the SVM algorithm. The 3-fold cross validation (3-CV) accuracy of the SVM model established based on extracting characteristic intensity data from spectra pretreated by first derivation was 98.61%, and the prediction accuracies of the training set and validation set were 100%. As for the PCA-SVM model, when the spectra pretreated by vector normalization and the number of principal components (NPC) is 7, the 3-CV accuracy and prediction accuracies all reached 100%. Both models have good performance and strong prediction capacity. These results demonstrate that Raman spectroscopy combined with a powerful SVM algorithm has great potential for providing an effective and accurate identification method for mineral TCMs.

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