Evaluation of GA-SVR method for modeling bed load transport in gravel-bed rivers

Roushangar, Kiyoumars; Koosheh, Ali

doi:10.1016/j.jhydrol.2015.06.006

Cited by 55 publications

(25 citation statements)

References 22 publications

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“…The fruit fly optimization algorithm (FOA) was involved in SVR for predicting the number of vacant parking spaces after a specific period of time [14]. The genetic algorithm (GA) was applicably administered to determine optimal SVR parameters for forecasting bed load transport rates of three gravel-bed rivers [15].…”

Section: Introductionmentioning

confidence: 99%

Parameter Optimization of Support Vector Regression Using Henry Gas Solubility Optimization Algorithm

Cao

Liu

2020

IEEE Access

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Support vector regression (SVR) is one of the most powerful and widely used machine learning algorithms regarding prediction. The kernel type, penalty factor and other parameters influence the efficiency and performance of SVR deeply. The optimization of these parameters is held a hot issue. In this work, we propose a SVR based prediction approach using henry gas solubility optimization algorithm (HGSO), which is a recent meta-heuristic algorithm inspired by Henry's law. First, SVR parameters are randomly generated in some certain ranges to form parameter population. Second, the prediction accuracies (PAs) are obtained using the population and SVR. Thirdly, the population and optimal SVR parameters are updated via PAs and HGSO. We repeat the second and third steps until the cutoff conditions are met. Ten low-and high-dimensional benchmark data sets are utilized to assess the prediction accuracy, convergence performance and computational complexity of the presented approach and other well-known algorithms. The experimental results reveal that our approach has the optimum comprehensive performance.

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Section: Introductionmentioning

confidence: 99%

Parameter Optimization of Support Vector Regression Using Henry Gas Solubility Optimization Algorithm

Cao

Liu

2020

IEEE Access

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“…On the other hand, the setting of the SVR model parameters directly affects the performance of the model, and the problem of setting the model parameters is still not well solved [17]. Sun et al (2009) [24] and Roushangar et al (2015) [25] proposed a hybrid calculation system in which genetic algorithm (GA) was adopted to search for the SVR Figure 1. The development of a water-conducting fracture zone (WCFZ) in mining overburden strata after mining.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the setting of the SVR model parameters directly affects the performance of the model, and the problem of setting the model parameters is still not well solved [17]. Sun et al (2009) [24] and Roushangar et al (2015) [25] proposed a hybrid calculation system in which genetic algorithm (GA) was adopted to search for the SVR parameters, Sustainability 2020, 12, 1809 3 of 15 and the results were encouraging. As a method to search for the optimal solution by simulating the natural evolution process, GA was first proposed by Holland (1975) [26].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Water-Conducting Fracture Zone (WCFZ) Height Using an MPGA-SVR Approach

et al. 2020

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Mine water that inrushes from coal-roof strata has always posed a substantial threat to mining activities every year. Therefore, an accurate prediction of the water-conducting fracture zone (WCFZ) height in the mining overburden strata is of great significance for the prevention and control of mine water accidents. The support vector regression (SVR) is proposed to predict the height of the WCFZ based on the mining depth, hard rock proportional coefficient, mining thickness and length of the working face. Simultaneously, the multi-population genetic algorithm (MPGA) is employed to search for the optimal SVR parameters. The MPGA-SVR model is trained and tested with a total of 69 collected data samples, and it is also applied to a field test. The accuracy and stability of the model were measured by the mean squared error and correlation coefficients. The obtained results show that the MPGA-SVR model achieves a higher accuracy and stability than the traditional empirical formula and genetic algorithm (GA)-SVR model. In terms of the process for optimizing the SVR parameters, the MPGA can find the optimal parameters more quickly and accurately, and it can effectively overcome the problem of premature and slow convergence of the genetic algorithm (GA). The proposed model improves the prediction accuracy and stability, which will help to avoid accidents caused by the inrush of water inrush in mining overburden strata and protect the ecological environment of the mining area.

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“…Artificial intelligences (AI), specially machine learning approaches, are remarkable forecasting tools which in the recent decade has been implemented in various fields of civil engineering studies (e.g., Sun et al 2014;Roushangar et al 2014a, b;Samui 2012;Koosheh 2015, Roushangar andAlizadeh 2015;Nourani et al 2016). The Gaussian process regressions (GPR) an effective kernel-based machine learning algorithm, is capable to be applied to probabilistic streamflow forecasting (Sun et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Forecasting Daily Seepage Discharge of an Earth Dam Using Wavelet–Mutual Information–Gaussian Process Regression Approaches

2016

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Because of their sensitive structure, earth dams might face failure due to seepage phenomenon. In order to prevent such failure, some equipment like piezometers are installed in the body or foundation of earth dams. This study investigated the importance of piezometer installation level in dam body or foundation using mutual information-wavelet-Gaussian process regression. 27 Piezometers in three section along with reservoir level were employed to predict onestep-ahead seepage discharge of Zonouz earth dam. The daily data of 1 year of piezometer level and reservoir level were collected for this purpose. In order to find the best possible input combination, three groups of modeling scenarios were defined using piezometers and reservoir level time series. As some input combinations had more than two variables, decomposed time series were imposed into mutual information (MI) tool in order to decrement input variables and find the most correlated input-output features. Afterward, mentioned features were imposed into optimized Gaussian process regression (GPR) to be predicted. Different kernels were selected as core tool of GPR, but results demonstrated the capability of radial basis function (RBF) kernel. GPR-RBF structure were optimized using cross-validation technique. Results indicated that input combination including piezometer level and reservoir level of section II, especially piezometer 203 time series led to the best result among all scenarios.

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Evaluation of GA-SVR method for modeling bed load transport in gravel-bed rivers

Cited by 55 publications

References 22 publications

Parameter Optimization of Support Vector Regression Using Henry Gas Solubility Optimization Algorithm

Parameter Optimization of Support Vector Regression Using Henry Gas Solubility Optimization Algorithm

Predicting the Water-Conducting Fracture Zone (WCFZ) Height Using an MPGA-SVR Approach

Forecasting Daily Seepage Discharge of an Earth Dam Using Wavelet–Mutual Information–Gaussian Process Regression Approaches

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