Application of support vector machines in scour prediction on grade-control structures

Goel, Arun; Pal, Mahesh

doi:10.1016/j.engappai.2008.05.008

Cited by 71 publications

(25 citation statements)

References 14 publications

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“…In the last decade, several studies reported the use of support vector regression (SVR) in civil and water resources engineering related applications (Dibike et al, 2001;Cigizoglu, 2005;Pal and Goel, 2006;Firat and Gungor, 2009;Goel and Pal, 2009;Goyal and Ojha, 2011). The results obtained from these studies showed that the SVR provided better results compared with the neural network approach.…”

Section: Introductionmentioning

confidence: 93%

Predicting time-dependent pier scour depth with support vector regression

Hong

Goyal

Chiew

et al. 2012

Journal of Hydrology

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Section: Introductionmentioning

confidence: 93%

Predicting time-dependent pier scour depth with support vector regression

Hong

Goyal

Chiew

et al. 2012

Journal of Hydrology

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“…A well-advised application of this tool is to select an appropriate covariance function or kernel and tune related hyper parameters. A number of kernels are discussed by researchers, but studies suggest the effectiveness of radial basis kernel function in the case of machine learning approaches in the majority of civil engineering applications (Gill et al 2006;Goel and Pa 2009;Nourani et al 2016). In the present study different GPR structures were analyzed via various kernel functions and mentioned input combinations.…”

Section: Gpr Modeling Developmentmentioning

confidence: 98%

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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“…i and ξ . i are slack variables specifying the upper and lower calibration error subject to ε, respectively [41].…”

Section: Svr Modelmentioning

confidence: 99%

Vehicular CO Emission Prediction Using Support Vector Regression Model and GIS

2018

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Transportation infrastructures play a significant role in the economy as they provide accessibility services to people. Infrastructures such as highways, road networks, and toll plazas are rapidly growing based on changes in transportation modes, which consequently create congestions near toll plaza areas and intersections. These congestions exert negative impacts on human health and the environment because vehicular emissions are considered as the main source of air pollution in urban areas and can cause respiratory and cardiovascular diseases and cancer. In this study, we developed a hybrid model based on the integration of three models, correlation-based feature selection (CFS), support vector regression (SVR), and GIS, to predict vehicular emissions at specific times and locations on roads at microscale levels in an urban areas of Kuala Lumpur, Malaysia. The proposed model comprises three simulation steps: first, the selection of the best predictors based on CFS; second, the prediction of vehicular carbon monoxide (CO) emissions using SVR; and third, the spatial simulation based on maps by using GIS. The proposed model was developed with seven road traffic CO predictors selected via CFS (sum of vehicles, sum of heavy vehicles, heavy vehicle ratio, sum of motorbikes, temperature, wind speed, and elevation). Spatial prediction was conducted based on GIS modelling. The vehicular CO emissions were measured continuously at 15 min intervals (recording 15 min averages) during weekends and weekdays twice per day (daytime, evening-time). The model's results achieved a validation accuracy of 80.6%, correlation coefficient of 0.9734, mean absolute error of 1.3172 ppm and root mean square error of 2.156 ppm. In addition, the most appropriate parameters of the prediction model were selected based on the CFS model. Overall, the proposed model is a promising tool for traffic CO assessment on roads.

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Application of support vector machines in scour prediction on grade-control structures

Cited by 71 publications

References 14 publications

Predicting time-dependent pier scour depth with support vector regression

Predicting time-dependent pier scour depth with support vector regression

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

Vehicular CO Emission Prediction Using Support Vector Regression Model and GIS

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