scholarly journals Riprap incipient motion for overtopping flows with machine learning models

2020 ◽  
Vol 22 (4) ◽  
pp. 749-767 ◽  
Author(s):  
Mohammad Najafzadeh ◽  
Giuseppe Oliveto
Keyword(s):  
Froude Number ◽  
Absolute Error ◽  
Multivariate Adaptive Regression Splines ◽  
Support Vector ◽  
Empirical Equations ◽  
Incipient Motion ◽  
Densimetric Froude Number ◽  
Uniformity Coefficient ◽  
Erosion Processes ◽  
Statistical Measures

Abstract Riprap stones are frequently applied to protect rivers and channels against erosion processes. Many empirical equations have been proposed in the past to estimate the unit discharge at the failure circumstance of riprap layers. However, these equations lack general impact due to the limited range of experimental variables. To overcome these shortcomings, support vector machine (SVM), multivariate adaptive regression splines (MARS), and random forest (RF) techniques have been applied in this study to estimate the approach densimetric Froude number at the incipient motion of riprap stones. Riprap stone size, streambank slope, uniformity coefficient of riprap layer stone, specific density of stones, and thickness of riprap layer have been considered as controlling variables. Quantitative performances of the artificial intelligence (AI) models have been assessed by many statistical measures including: coefficient of correlation (R), root mean square error (RMSE), mean absolute error (MAE), and scatter index (SI). Statistical performance of AI models indicated that SVM model with radial basis function (RBF) kernel had better performance (SI = 0.37) than MARS (SI = 0.75) and RF (SI = 0.63) techniques. The proposed AI models performed better than existing empirical equations. From a parametric study the results demonstrated that the erosion-critical stone-referred Froude number (Fs,c) is mainly controlled by the streambank slope.

scholarly journals Estimating daily reference evapotranspiration using hybrid gamma test-least square support vector machine, gamma test-ANN, and gamma test-ANFIS models in an arid area of Iran

2018 ◽  
Vol 11 (1) ◽  
pp. 217-240 ◽  
Author(s):  
Akram Seifi ◽  
Hossien Riahi
Keyword(s):  
Support Vector Machine ◽  
Absolute Error ◽  
Least Square ◽  
Support Vector ◽  
Empirical Equations ◽  
Gamma Test ◽  
Hybrid Approaches ◽  
Input Variables ◽  
Predicted Values ◽  
Better Than

Abstract In this study, a hybrid model of least square support vector machine-gamma test (LSSVM-GT) is proposed for estimating daily ETo under arid conditions of Zahedan station, Iran. Gamma test was used for selecting the best input vectors for models. The estimated ETo by LSSVM-GT model with different kernels of RBF, linear and polynomial, were compared with other hybrid approaches including ANN-GT, ANFIS-GT, and empirical equations. The gamma test revealed that climate variables of minimum and maximum air temperature and wind speed are the most important parameters. The LSSVM model performed better than the ANFIS and ANN models when similar meteorological input variables are used. Also, the performance of the three models of LSSVM, ANFIS, and ANN were better than the empirical equations such as Blaney–Criddle and Hargreaves–Samani. The RMSE, MAE, and R2 for the best input vector by LSSVM were 0.1 mm day−1, 0.13 mm day−1, and 0.99, respectively. The threshold of relative absolute error of 95% predicted values by LSSVM, ANN, and ANFIS models were about 8.4%, 9.4%, and 24%, respectively. Based on the comparison of the overall performances, the developed LSSVM-GT approach is greatly capable of providing favorable predictions with high precision in arid regions of Iran.

scholarly journals Interfacial Friction Prediction in a Vertical Annular Two-Phase Flow Based on Support Vector Regression Machine

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3609
Author(s):  
Qiang Liu ◽  
Xingya Feng ◽  
Junru Chen
Keyword(s):  
Reynolds Number ◽  
Support Vector Regression ◽  
Froude Number ◽  
Friction Factor ◽  
Absolute Error ◽  
Interfacial Friction ◽  
Support Vector ◽  
Optimal Model ◽  
Two Phase ◽  
Friction Prediction

Accurate prediction of interfacial friction factor is critical for calculation of pressure drop and investigation of flow mechanism of vertical annular two-phase flows. Theoretical models of interfacial friction factor based on physical insight have been developed; however, these are inconvenient in engineering practice as too many parameters need to be measured. Although many researchers have proposed various empirical correlations to improve computation efficiency, there is no generally accepted simple formula. In this study, an efficient prediction model based on support vector regression machine (SVR) is proposed. Through sensitivity analysis, five factors are determined as the input parameters to train the SVR model, relative liquid film thickness, liquid Reynolds number, gas Reynolds number, liquid Froude number and gas Froude number. The interfacial friction factor is chosen as the output parameter to check the overall performance of the model. With the help of particle swarm algorithm, the optimization process is accelerated considerably, and the optimal model is obtained through iterations. Compared with other correlations, the optimal model shows the lowest average absolute error (AAE of 0.0004), lowest maximum absolute error (MAE of 0.006), lowest root mean square error (RMSE of 0.00076) and highest correlation factor (r of 0.995). The analysis using various data in the literature demonstrates its accuracy and stability in interfacial friction prediction. In summary, the proposed machine learning model is effective and can be applied to a wider range of conditions for vertical annular two-phase flows.

scholarly journals Experimental studies on scour of supercritical flow jets in upstream of screens and modelling scouring dimensions using artificial intelligence to combine multiple models (AIMM)

2019 ◽  
Vol 21 (5) ◽  
pp. 893-907 ◽  
Author(s):  
Sina Sadeghfam ◽  
Rasoul Daneshfaraz ◽  
Rahman Khatibi ◽  
Omar Minaei
Keyword(s):  
Artificial Intelligence ◽  
Froude Number ◽  
Experimental Studies ◽  
Support Vector ◽  
Densimetric Froude Number ◽  
Number Range ◽  
Artificial Intelligence Models ◽  
Level 1 ◽  
Supercritical Flows ◽  
Level 2

Abstract Performances of screens in watercourses are investigated for dissipating energy of supercritical flows, capable of inducing scour or stabilising hydraulic jumps. Subsequent scouring pits are characterised by pit depth and pit length. Inherent processes are studied through laboratory tests by producing a set of empirical data to formulate a model of the scour for explaining subsequent processes. The experimental set-up comprises: (i) Froude number of supercritical flows (range: 3.5–8.0); (ii) particle densimetric Froude number (range: 2–10) using five granular samples; and (iii) two screen porosities (40% and 50%). Trained and tested artificial intelligence models explain the data by expressing depth and length of the pit through the following levels: Level 1: use the experimental data and test the models of: Sugeno fuzzy logic (SFL) and neuro-fuzzy (NF); and Level 2: use outputs of Level 1 models as inputs to support vector machine (SVM). The results reveal that the Level 2 model improves model performances compared with the single models with respect to R2, root mean square error (RMSE), Nash–Sutcliffe coefficient (NSC) and residual errors. While Level 1 models remain fit-for-purpose, the comparative improvement from Level 1 to Level 2 can be as high as 58% in terms of NSC for the testing phase.

scholarly journals Effect of Dimensionality Reductions Technique in Modelling and Forecasting River Flow

2018 ◽  
Vol 7 (4.30) ◽  
pp. 573
Author(s):  
Shuhaida Ismail ◽  
Ani Shabri ◽  
Aida Mustapha ◽  
Siraj Mohammed Pandhiani
Keyword(s):  
Support Vector Machine ◽  
River Flow ◽  
Principal Component ◽  
Absolute Error ◽  
Least Square ◽  
Support Vector ◽  
Accurate Information ◽  
Statistical Measures ◽  
Future Value ◽  
Predicted Values

The ability of obtain accurate information on future river flow is a fundamental key for water resources planning, and management. Traditionally, single models have been introduced to predict the future value of river flow. This paper investigates the ability of Principal Component Analysis as dimensionality reduction technique and combined with single Support Vector Machine and Least Square Support Vector Machine, referred to as PCA-SVM and PCA-LSSVM. This study also presents comparison between the proposed models with single models of SVM and LSSVM. These models are ranked based on four statistical measures namely Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Correlation Coefficient ( ), and Correlation of Efficiency (CE). The results shows that PCA combined with LSSVM has better performance compared to other models. The best ranked models are then measured using Mean of Forecasting Error (MFE) to determine its forecast rate. PCA-LSSVM proven to be better model as it also indicates a small percentage of under-predicted values compared to the observed river flow values of 0.89% for Tualang river while over-predicted by 2. 08% for Bernam river. The study concludes by recommending the PCA as dimension reduction approach combined with LSSVM for river flow forecasting due to better prediction results and stability than those achieved from single models  

scholarly journals Enhancing Monthly Lake Levels Forecasting Using Heuristic Regression Techniques with Periodicity Data Component: Application of Lake Michigan

2021 ◽  
Author(s):  
Vahdettin DEMIR
Keyword(s):  
Lake Michigan ◽  
Absolute Error ◽  
Least Square ◽  
Multivariate Adaptive Regression Splines ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Lake Levels ◽  
M5 Model Tree ◽  
Comparison Results ◽  
Periodicity Component

Abstract This paper investigates the accuracy of three different techniques with periodicity component for estimation of monthly lake levels. The compared methods are Least Square Support Vector Regression (LSSVR) Multivariate Adaptive Regression Splines (MARS) and M5 Model Tree (M5-Tree). Data from Lake Michigan, located in the USA, is used in the analysis. In the first stage of the study, three different techniques were applied to forecast monthly lake-levels variations up to 8- mount ahead of time intervals. In the second stage, the influence of the periodicity component was applied (month number of the year, e.g., 1, 2, 3, …12) as an external sub-set in modeling monthly lake levels. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2) were utilized are used for evaluating the accuracy of models. In both stages, the comparison results indicate that the MARS model generally performs superior to the LSSVR, and M5-Tree models. Furthermore, it has been discovered that including periodicity as an input to the models improves their accuracy in projecting monthly lake levels.

Experimental Studies of Sedimentation Basin Dynamics

1977 ◽  
Vol 12 (1) ◽  
pp. 77-90
Author(s):  
J.F. Cordoba-Molina ◽  
P.L. Silveston ◽  
R. R. Hudgins
Keyword(s):  
Dynamic Response ◽  
Froude Number ◽  
Flow Model ◽  
Experimental Studies ◽  
Densimetric Froude Number ◽  
Number Range ◽  
The Real ◽  
Highly Correlated ◽  
Sedimentation Basins ◽  
Simple Flow

Abstract A simple Flow Model is proposed to describe the dynamic response of sedimentation basins. The response predicted by this model is linear as opposed to the real response of the basin which is nonlinear. However, the real response of the basin is highly correlated with its densimetric Froude number, and as a consequence our linear model effectively predicts the response of the basin in a restricted densimetric Froude Number range. Our experiments show that the response of the basin becomes more sluggish and erratic as the densimetric Froude number decreases.

scholarly journals Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements

2021 ◽  
Vol 10 (1) ◽  
pp. 42
Author(s):  
Kieu Anh Nguyen ◽  
Walter Chen ◽  
Bor-Shiun Lin ◽  
Uma Seeboonruang
Keyword(s):  
Machine Learning ◽  
Soil Erosion ◽  
Ensemble Methods ◽  
Machine Learning Algorithms ◽  
Multivariate Adaptive Regression Splines ◽  
Gradient Boosting ◽  
Support Vector ◽  
Ensemble Machine Learning ◽  
Boosting Method ◽  
Bagging Method

Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble machine learning algorithms to the Shihmen Reservoir watershed in northern Taiwan. Three categories of ensemble methods were considered in this study: (a) Bagging, (b) boosting, and (c) stacking. The bagging method in this study refers to bagged multivariate adaptive regression splines (bagged MARS) and random forest (RF), and the boosting method includes Cubist and gradient boosting machine (GBM). Finally, the stacking method is an ensemble method that uses a meta-model to combine the predictions of base models. This study used RF and GBM as the meta-models, decision tree, linear regression, artificial neural network, and support vector machine as the base models. The dataset used in this study was sampled using stratified random sampling to achieve a 70/30 split for the training and test data, and the process was repeated three times. The performance of six ensemble methods in three categories was analyzed based on the average of three attempts. It was found that GBM performed the best among the ensemble models with the lowest root-mean-square error (RMSE = 1.72 mm/year), the highest Nash-Sutcliffe efficiency (NSE = 0.54), and the highest index of agreement (d = 0.81). This result was confirmed by the spatial comparison of the absolute differences (errors) between model predictions and observations using GBM and RF in the study area. In summary, the results show that as a group, the bagging method and the boosting method performed equally well, and the stacking method was third for the erosion pin dataset considered in this study.

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.

scholarly journals Feature Extraction in Six Blocks to Detect and Recognize English Numbers

2021 ◽  
pp. 3790-3803
Author(s):  
Heba Kh. Abbas ◽  
Haidar J. Mohamad
Keyword(s):  
Fuzzy Logic ◽  
Feature Extraction ◽  
Computational Cost ◽  
Absolute Error ◽  
Suggested Method ◽  
Support Vector ◽  
Horizontal Line ◽  
Svm Algorithm ◽  
Fuzzy Logic Method ◽  
Logic Method

    The Fuzzy Logic method was implemented to detect and recognize English numbers in this paper. The extracted features within this method make the detection easy and accurate. These features depend on the crossing point of two vertical lines with one horizontal line to be used from the Fuzzy logic method, as shown by the Matlab code in this study. The font types are Times New Roman, Arial, Calabria, Arabic, and Andalus with different font sizes of 10, 16, 22, 28, 36, 42, 50 and 72. These numbers are isolated automatically with the designed algorithm, for which the code is also presented. The number’s image is tested with the Fuzzy algorithm depending on six-block properties only. Groups of regions (High, Medium, and Low) for each number showed unique behavior to recognize any number. Normalized Absolute Error (NAE) equation was used to evaluate the error percentage for the suggested algorithm. The lowest error was 0.001% compared with the real number. The data were checked by the support vector machine (SVM) algorithm to confirm the quality and the efficiency of the suggested method, where the matching was found to be 100% between the data of the suggested method and SVM. The six properties offer a new method to build a rule-based feature extraction technique in different applications and detect any text recognition with a low computational cost.

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