scholarly journals Evaluation of the effective parameters on energy losses of rectangular and circular culverts via kernel-based approaches

2019 ◽  
Vol 21 (6) ◽  
pp. 1014-1029 ◽  
Author(s):  
Kiyoumars Roushangar ◽  
Ghazaleh Nasssaji Matin ◽  
Roghayeh Ghasempour ◽  
Seyed Mahdi Saghebian
Keyword(s):  
Uncertainty Analysis ◽  
Froude Number ◽  
Gene Expression Programming ◽  
Gaussian Process Regression ◽  
Performance Criteria ◽  
Bend Angle ◽  
Support Vector ◽  
Energy Losses ◽  
Effective Parameters ◽  
Model Efficiency

Abstract Energy dissipation in culverts is a complex phenomenon due to the nonlinearity and uncertainties of the process. In the current study, the capability of Gaussian process regression (GPR) and support vector machine (SVM) as kernel-based approaches and the gene expression programming (GEP) method was assessed in predicting energy losses in culverts. Two types of bend loss in rectangular culverts and entrance loss in circular culverts with different inlet end treatments were considered. Various input combinations were developed and tested using experimental data. The OAT (one-at-a-time), factorial sensitivity analysis and Monte Carlo uncertainty analysis were used to select the effective parameters in modeling. The results of performance criteria proved the capability of the applied methods (i.e. high correlation coefficient (R) and determination coefficient (DC) and low root mean square error (RSME)). For rectangular culverts, the model with parameters Fr (Froude number) and θ (bend angle), and for circular culverts, the model with parameters Fr and Hw/D (depth ratio), were the superior models. It showed that using the bend downstream Froude number caused an increment in model efficiency. Among the four end inlet treatments, mitered flush to 1.5:1 fill slope inlet yielded more accurate prediction. The sensitivity and uncertainty analysis showed that θ and Hw/D had the most significant impact on modeling, and Fr had the highest uncertainty.

scholarly journals Using data mining methods to improve discharge coefficient prediction in Piano Key and Labyrinth weirs

2021 ◽  
Author(s):  
Mahdi Majedi-Asl ◽  
Mehdi Foladipanah ◽  
Venkat Arun ◽  
Ravi Prakash Tripathi
Keyword(s):  
Discharge Coefficient ◽  
Gene Expression Programming ◽  
Research Work ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Effective Parameters ◽  
Data Set ◽  
Labyrinth Weir ◽  
Dimensional Parameters ◽  
Using Data

Abstract As a remarkable parameter, the discharge coefficient (Cd) plays an important role in determining weirs' passing capacity. In this research work, the support vector machine (SVM) and the gene expression programming (GEP) algorithms were assessed to predict Cd of piano key weir (PKW), rectangular labyrinth weir (RLW), and trapezoidal labyrinth weir (TLW) with gathered experimental data set. Using dimensional analysis, various combinations of hydraulic and geometric non-dimensional parameters were extracted to perform simulation. The superior model for the SVM and the GEP predictor for PKW, RLW, and TLW included , and respectively. The results showed that both algorithms are potential in predicting discharge coefficient, but the coefficient of determination (RMSE, R2, Cd(DDR)max) illustrated the superiority of the GEP performance over the SVM. The results of the sensitivity analysis determined the highest effective parameters for PKW, RLW, and TLW in predicting discharge coefficients are , , and Fr respectively.

scholarly journals Dam failure peak outflow prediction through GEP-SVM meta models and uncertainty analysis

2021 ◽  
Author(s):  
Mohammad Nobarinia ◽  
Farhoud Kalateh ◽  
Vahid Nourani ◽  
Alireza Babaeian Amini
Keyword(s):  
Uncertainty Analysis ◽  
Failure Time ◽  
Gene Expression Programming ◽  
Soil Characteristics ◽  
Dam Failure ◽  
Support Vector ◽  
Applied Model ◽  
Peak Outflow ◽  
Learning Machine ◽  
The Impact

Abstract Accurate prediction of a breached dam's peak outflow is a significant factor for flood risk analysis. In this study, the capability of Support Vector Machine and Kernel Extreme Learning Machine as kernel-based approaches and Gene Expression Programming method was assessed in breached dam peak outflow prediction. Two types of modeling were considered. First, only dam reservoir height and volume at the failure time were used as the input combinations (state 1). Then, soil characteristics were added to input combinations to investigate particularly the impact of soil characteristics (state 2). Results showed that the use of only soil characteristics did not lead to a desired accuracy; however, adding soil characteristics to input combinations (state 2) improved the models' accuracy up to 40%. The outcome of the applied models was also compared with existing empirical equations and it was found the applied models yielded better results. Sensitivity analysis results showed that dam height had the most important role in the peak outflow prediction, while the strength parameters did not have significant impacts. Furthermore, for assessing the best-applied model dependability, uncertainty analysis was used and the results indicated that the SVM model had an allowable degree of uncertainty in peak outflow modelling.

scholarly journals Uncertainty analyses regarding evaluating effective parameters on the hydraulic jump characteristics of different shape channels

2021 ◽  
Author(s):  
Kiyoumars Roushangar ◽  
Farzin Homayounfar ◽  
Roghayeh Ghasempour
Keyword(s):  
Hydraulic Jump ◽  
Gaussian Process Regression ◽  
Support Vector ◽  
Hydraulic Jumps ◽  
Empirical Equations ◽  
Effective Parameters ◽  
Jump Length ◽  
Depth Ratio ◽  
Sequent Depth ◽  
Semi Empirical

Abstract The hydraulic jump phenomenon is a beneficial tool in open channels for dissipating the extra energy of the flow. The sequent depth ratio and hydraulic jump length critically contribute to designing hydraulic structures. In this research, the capability of Support Vector Machine (SVM) and Gaussian Process Regression (GPR) as kernel-based approaches was evaluated to estimate the features of submerged and free hydraulic jumps in channels with rough elements and various shapes, followed by comparing the findings of GPR and SVM models and the semi-empirical equations. The results represented the effect of the geometry (i.e., steps and roughness elements) of the applied appurtenances on hydraulic jump features in channels with appurtenances. Moreover, the findings confirmed the significance of the upstream Froude number in the sequent depth ratio estimating in submerged and free hydraulic jumps. In addition, the immersion was the highest contributing variable regarding the submerged jump length on sloped smooth bed and horizontal channels. Based on the comparisons among kernel-based approaches and the semi-empirical equations, kernel-based models showed better performance than these equations. Finally, an uncertainty analysis was conducted to assess the dependability of the best applied model. The results revealed that the GRP model possesses an acceptable level of uncertainty in the modeling process.

scholarly journals Forecasting Factor of Safety of Slopes Stability Using Several Machine Learning Techniques

2021 ◽  
Author(s):  
Arsalan Mahmoodzadeh ◽  
Mokhtar Mohammadi
Keyword(s):  
Machine Learning ◽  
Slope Stability ◽  
Factor Of Safety ◽  
Slope Failure ◽  
Short Term Memory ◽  
Gaussian Process Regression ◽  
Machine Learning Techniques ◽  
Unit Weight ◽  
Support Vector ◽  
Effective Parameters

Abstract Because of the disasters associated with slope failure, the analysis and forecasting of slope stability for geotechnical engineers are crucial. In this work, in order to forecast the factor of safety (FOS) of the slopes, six machine learning (ML) techniques of Gaussian process regression (GPR), support vector regression (SVR), decision trees (DT), long-short term memory (LSTM), deep neural networks (DNN), and K-nearest neighbors (KNN) were performed. A total of 327 slope cases in Iran with various geometric and shear strength parameters analyzed by PLAXIS software to evaluate their FOS, were employed in the models. The K-fold (K=5) cross-validation (CV) method was applied to evaluate the performance of models’ prediction. Finally, all the models produced acceptable results and almost close to each other. However, the GPR model with R2 = 0.8139, RMSE = 0.160893, and MAPE = 7.209772%, was the most accurate model to predict slope stability. Also, the backward selection method was applied to evaluate the contribution of each parameter in the prediction problem. The results showed that all the features considered in this study have significant contributions to slope stability. However, features φ (friction angle) and γ (unit weight) were the most effective and least effective parameters on slope stability, respectively.

scholarly journals Application of Support Vector Machine and Gene Expression Programming on Tropospheric ozone Prognosticating for Tehran Metropolitan

2017 ◽  
Vol 3 (8) ◽  
pp. 557 ◽  
Author(s):  
Vahid Mehdipour ◽  
Mahsa Memarianfard
Keyword(s):  
Gene Expression ◽  
Support Vector Machine ◽  
Tropospheric Ozone ◽  
Meteorological Parameters ◽  
Gene Expression Programming ◽  
Input Parameter ◽  
Developed Countries ◽  
Support Vector ◽  
Effective Parameters ◽  
Measurement Tools

Air pollution became fatal issue for humanity and all environment and developed countries unanimously allocated vast investments on monitoring and researches about air pollutants. Soft computing as a novel way for pollutants prediction can be used for measurement tools calibration which can coincidently decrease the expenditures and enhance their ability to adapt quickly. In this paper support vector machine (SVM) and gene expression programming (GEP) as two powerful approaches with reliable results in previous studies, used to predict tropospheric ozone in Tehran metropolitan by using the photochemical precursors and meteorological parameters as predictors. In a comparison between the two approaches, the best model of SVM gave superior results as it depicted the RMSE= 0.0774 and R= 0.8459 while these results of gene expression programming, respectively, are 0.0883 and 0.7938. Sensitivity of O3 against photochemical precursors and meteorological parameters and also for every input parameter, has been analysed discreetly and the gained results imply that PM2.5, PM10, temperature, CO and NO2 are the most effective parameters for O3 values tolerances. For SVM, several kernel tricks used and the best appropriate kernel selected due to its result. Nonetheless, gamma and sin2 values varied for every kernel and in the last radial basis function kernel opted as the best trick in this study. Finally, the best model of both applications revealed, and the resulted models evaluated as reliable and acceptable.

scholarly journals Modeling of Cutting Force in the Turning of AISI 4340 Using Gaussian Process Regression Algorithm

2021 ◽  
Vol 11 (9) ◽  
pp. 4055
Author(s):  
Mahdi S. Alajmi ◽  
Abdullah M. Almeshal
Keyword(s):  
Gaussian Process ◽  
Cutting Force ◽  
Predictive Accuracy ◽  
Gaussian Process Regression ◽  
Machining Process ◽  
Support Vector ◽  
Process Data ◽  
Cutting Force Prediction ◽  
Artificial Neural Network Ann ◽  
Aisi 4340

Machining process data can be utilized to predict cutting force and optimize process parameters. Cutting force is an essential parameter that has a significant impact on the metal turning process. In this study, a cutting force prediction model for turning AISI 4340 alloy steel was developed using Gaussian process regression (GPR), support vector machines (SVM), and artificial neural network (ANN) methods. The GPR simulations demonstrated a reliable prediction of surface roughness for the dry turning method with R2 = 0.9843, MAPE = 5.12%, and RMSE = 1.86%. Performance comparisons between GPR, SVM, and ANN show that GPR is an effective method that can ensure high predictive accuracy of the cutting force in the turning of AISI 4340.

scholarly journals Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels

2021 ◽  
Vol 11 (8) ◽  
pp. 3705
Author(s):  
Jie Zeng ◽  
Panayiotis C. Roussis ◽  
Ahmed Salih Mohammed ◽  
Chrysanthos Maraveas ◽  
Seyed Alireza Fatemi ◽  
...  
Keyword(s):  
Radial Basis Function ◽  
Particle Velocity ◽  
Basis Function ◽  
Absolute Error ◽  
Peak Particle Velocity ◽  
Support Vector ◽  
Effective Parameters ◽  
Experimental Database ◽  
Interaction Detection ◽  
Radial Basis

This research examines the feasibility of hybridizing boosted Chi-Squared Automatic Interaction Detection (CHAID) with different kernels of support vector machine (SVM) techniques for the prediction of the peak particle velocity (PPV) induced by quarry blasting. To achieve this objective, a boosting-CHAID technique was applied to a big experimental database comprising six input variables. The technique identified four input parameters (distance from blast-face, stemming length, powder factor, and maximum charge per delay) as the most significant parameters affecting the prediction accuracy and utilized them to propose the SVM models with various kernels. The kernel types used in this study include radial basis function, polynomial, sigmoid, and linear. Several criteria, including mean absolute error (MAE), correlation coefficient (R), and gains, were calculated to evaluate the developed models’ accuracy and applicability. In addition, a simple ranking system was used to evaluate the models’ performance systematically. The performance of the R and MAE index of the radial basis function kernel of SVM in training and testing phases, respectively, confirm the high capability of this SVM kernel in predicting PPV values. This study successfully demonstrates that a combination of boosting-CHAID and SVM models can identify and predict with a high level of accuracy the most effective parameters affecting PPV values.

Comparisons of Machine Learning Methods of Statistical Downscaling Method: Case Studies of Daily Climate Anomalies in Thailand

2021 ◽  
Author(s):  
Kanawut Chattrairat ◽  
Waranyu Wongseree ◽  
Adisorn Leelasantitham
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Performance Criteria ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Support Vector ◽  
Seasonal Temperature ◽  
Statistical Downscaling Method ◽  
Downscaling Method

The climate change which is essential for daily life and especially agriculture has been forecasted by global climate models (GCMs) in the past few years. Statistical downscaling method (SD) has been used to improve the GCMs and enables the projection of local climate. Many pieces of research have studied climate change in case of individually seasonal temperature and precipitation for simulation; however, regional difference has not been included in the calculation. In this research, four fundamental SDs, linear regression (LR), Gaussian process (GP), support vector machine (SVM) and deep learning (DL), are studied for daily maximum temperature (TMAX), daily minimum temperature (TMIN), and precipitation (PRCP) based on the statistical relationship between the larger-scale climate predictors and predictands in Thailand. Additionally, the data sets of climate variables from over 45 weather stations overall in Thailand are used to calculate in this calculation. The statistical analysis of two performance criteria (correlation and root mean square error (RMSE)) shows that the DL provides the best performance for simulation. The TMAX and TMIN were calculated and gave a similar trend for all models. PRCP results found that in the North and South are adequate and poor performance due to high and low precipitation, respectively. We illustrate that DL is one of the suitable models for the climate change problem.

scholarly journals Comparative analysis of Kernel-based versus BFGS-ANN and deep learning methods in monthly reference evaporation estimation

2020 ◽  
Author(s):  
Mohammad Taghi Sattari ◽  
Halit Apaydin ◽  
Shahab Shamshirband ◽  
Amir Mosavi
Keyword(s):  
Deep Learning ◽  
Minimum Temperature ◽  
Meteorological Parameters ◽  
Short Term Memory ◽  
Sunshine Duration ◽  
Gaussian Process Regression ◽  
Support Vector ◽  
Ann Model ◽  
Learning Methods ◽  
Average Maximum

Abstract. Proper estimation of the reference evapotranspiration (ET0) amount is an indispensable matter for agricultural water management in the efficient use of water. The aim of study is to estimate the amount of ET0 with a different machine and deep learning methods by using minimum meteorological parameters in the Corum region which is an arid and semi-arid climate with an important agricultural center of Turkey. In this context, meteorological variables of average, maximum and minimum temperature, sunshine duration, wind speed, average, maximum, and minimum relative humidity are used as input data monthly. Two different kernel-based (Gaussian Process Regression (GPR) and Support Vector Regression (SVR)) methods, BFGS-ANN and Long short-term memory models were used to estimate ET0 amounts in 10 different combinations. According to the results obtained, all four methods used predicted ET0 amounts in acceptable accuracy and error levels. BFGS-ANN model showed higher success than the others. In kernel-based GPR and SVR methods, Pearson VII function-based universal kernel was the most successful kernel function. Besides, the scenario that is related to temperature in all scenarios used, including average temperature, maximum and minimum temperature, and sunshine duration gave the best results. The second-best scenario was the one that covers only the sunshine duration. In this case, the ANN (BFGS-ANN) model, which is optimized with the BFGS method that uses only the sunshine duration, can be estimated with the 0.971 correlation coefficient of ET0 without the need for other meteorological parameters.

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