Minimax Probability Machine

2015 ◽  
pp. 182-210
Author(s):  
Pijush Samui ◽  
Yıldırım Huseyin Dalkiliç ◽  
Hariharan Rajadurai ◽  
J. Jagan
Keyword(s):  
Earthquake Engineering ◽  
Cyclic Stress ◽  
Support Vector ◽  
Ground Acceleration ◽  
Cyclic Stress Ratio ◽  
Geotechnical Earthquake Engineering ◽  
Liquefaction Susceptibility ◽  
Minimax Probability Machine ◽  
Input Variables ◽  
Artificial Neural Network Ann

Liquefaction in soil is one of the other major problems in geotechnical earthquake engineering. This chapter adopts Minimax Probability Machine (MPM) for prediction of seismic liquefaction potential of soil based on Shear Wave Velocity (Vs) data. MPM has been used as a classification technique. Two models (MODEL I and MODEL II) have been adopted. In MODEL I, input variables are Cyclic Stress Ratio (CSR), and Vs MODEL II uses Peck Ground Acceleration (PGA) and Vs as input variables. The developed MPM has been compared with the Artificial Neural Network (ANN) and Support Vector Machine (SVM) models. The developed MPM is a robust tool for determination of liquefaction susceptibility of soil.

Utilization of Classification Techniques for the Determination of Liquefaction Susceptibility of Soils

2016 ◽  
pp. 124-160 ◽  
Author(s):  
J. Jagan ◽  
Prabhakar Gundlapalli ◽  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
Earthquake Engineering ◽  
Relevance Vector Machine ◽  
Least Square ◽  
Support Vector ◽  
Ground Acceleration ◽  
Geotechnical Earthquake Engineering ◽  
Liquefaction Susceptibility ◽  
Input Variables

The determination of liquefaction susceptibility of soil is a paramount project in geotechnical earthquake engineering. This chapter adopts Support Vector Machine (SVM), Relevance Vector Machine (RVM) and Least Square Support Vector Machine (LSSVM) for determination of liquefaction susceptibility based on Cone Penetration Test (CPT) from Chi-Chi earthquake. Input variables of SVM, RVM and LSSVM are Cone Resistance (qc) and Peak Ground Acceleration (amax/g). SVM, RVM and LSSVM have been used as classification tools. The developed SVM, RVM and LSSVM give equations for determination of liquefaction susceptibility of soil. The comparison between the developed models has been carried out. The results show that SVM, RVM and LSSVM are the robust models for determination of liquefaction susceptibility of soil.

Utilization of Classification Techniques for the Determination of Liquefaction Susceptibility of Soils

2018 ◽  
pp. 1507-1543
Author(s):  
J. Jagan ◽  
Prabhakar Gundlapalli ◽  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
Earthquake Engineering ◽  
Relevance Vector Machine ◽  
Least Square ◽  
Support Vector ◽  
Ground Acceleration ◽  
Geotechnical Earthquake Engineering ◽  
Liquefaction Susceptibility ◽  
Input Variables

The determination of liquefaction susceptibility of soil is a paramount project in geotechnical earthquake engineering. This chapter adopts Support Vector Machine (SVM), Relevance Vector Machine (RVM) and Least Square Support Vector Machine (LSSVM) for determination of liquefaction susceptibility based on Cone Penetration Test (CPT) from Chi-Chi earthquake. Input variables of SVM, RVM and LSSVM are Cone Resistance (qc) and Peak Ground Acceleration (amax/g). SVM, RVM and LSSVM have been used as classification tools. The developed SVM, RVM and LSSVM give equations for determination of liquefaction susceptibility of soil. The comparison between the developed models has been carried out. The results show that SVM, RVM and LSSVM are the robust models for determination of liquefaction susceptibility of soil.

scholarly journals Machine learning modelling for predicting soil liquefaction susceptibility

2011 ◽  
Vol 11 (1) ◽  
pp. 1-9 ◽  
Author(s):  
P. Samui ◽  
T. G. Sitharam
Keyword(s):  
Machine Learning ◽  
Standard Penetration Test ◽  
Soil Liquefaction ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Machine Learning Technique ◽  
Ground Acceleration ◽  
Cyclic Stress Ratio ◽  
Liquefaction Susceptibility ◽  
Learning Technique

Abstract. This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN) based on multi-layer perceptions (MLP) that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT [(N1)60] and cyclic stress ratio (CSR). Further, an attempt has been made to simplify the models, requiring only the two parameters [(N1)60 and peck ground acceleration (amax/g)], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

scholarly journals Analysis of Model Performance and Implementation of an Optimized Flood Prediction Model Using Data Mining Techniques

2021 ◽  
Vol 10 (2) ◽  
pp. 888-893
Keyword(s):  
Prediction Model ◽  
Model Performance ◽  
Warning System ◽  
Support Vector ◽  
Ann Model ◽  
Flood Prediction ◽  
River Region ◽  
Input Variables ◽  
Using Data ◽  
Artificial Neural Network Ann

Flooding is a major problem globally, and especially in SuratThani province, Thailand. Along the lower Tapeeriver in SuratThani, the population density is high. Implementing an early warning system can benefit people living along the banks here. In this study, our aim was to build a flood prediction model using artificial neural network (ANN), which would utilize water and stream levels along the lower Tapeeriver to predict floods. This model was used to predict flood using a dataset of rainfall and stream levels measured at local stations. The developed flood prediction model consisted of 4 input variables, namely, the rainfall amounts and stream levels at stations located in the PhraSeang district (X.37A), the Khian Sa district (X.217), and in the Phunphin district (X.5C). Model performance was evaluated using input data spanning a period of eight years (2011–2018). The model performance was compared with support vector machine (SVM), and ANN had better accuracy. The results showed an accuracy of 97.91% for the ANN model; however, for SVM it was 97.54%. Furthermore, the recall (42.78%) and f-measure (52.24%) were better for our model, however, the precision was lower. Therefore, the designed flood prediction model can estimate the likelihood of floods around the lower Tapee river region

scholarly journals River flow time series using least squares support vector machines

2011 ◽  
Vol 15 (6) ◽  
pp. 1835-1852 ◽  
Author(s):  
R. Samsudin ◽  
P. Saad ◽  
A. Shabri
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Hybrid Model ◽  
River Flow ◽  
Moving Average ◽  
Peninsular Malaysia ◽  
Support Vector ◽  
Group Method ◽  
Input Variables ◽  
Artificial Neural Network Ann

Abstract. This paper proposes a novel hybrid forecasting model known as GLSSVM, which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM). The GMDH is used to determine the useful input variables which work as the time series forecasting for the LSSVM model. Monthly river flow data from two stations, the Selangor and Bernam rivers in Selangor state of Peninsular Malaysia were taken into consideration in the development of this hybrid model. The performance of this model was compared with the conventional artificial neural network (ANN) models, Autoregressive Integrated Moving Average (ARIMA), GMDH and LSSVM models using the long term observations of monthly river flow discharge. The root mean square error (RMSE) and coefficient of correlation (R) are used to evaluate the models' performances. In both cases, the new hybrid model has been found to provide more accurate flow forecasts compared to the other models. The results of the comparison indicate that the new hybrid model is a useful tool and a promising new method for river flow forecasting.

Ground Motion Selection for Liquefaction Evaluation Analysis of Earthen Levees

2012 ◽  
Vol 28 (4) ◽  
pp. 1331-1351 ◽  
Author(s):  
Adda Athanasopoulos-Zekkos ◽  
Mustafa Saadi
Keyword(s):  
Ground Motion ◽  
Cross Sections ◽  
Ground Motions ◽  
Cyclic Stress ◽  
Cyclic Stress Ratio ◽  
Ground Motion Selection ◽  
Liquefaction Susceptibility ◽  
Wide Range ◽  
Dynamic Analyses ◽  
The Mean

Guidelines for selecting ground motions for liquefaction evaluation analysis of earthen levees are proposed. These guidelines were developed based on results from dynamic analyses of characteristic earthen levee cross sections using a wide range of ground motions (~1,500). The effect of a number of ground motion parameters on the dynamic response of the levees in terms of liquefaction susceptibility was studied, and the parameters that correlated best to the response were identified. For the liquefaction triggering evaluation, the mean period of the ground motion ( Tm) is best correlated to the cyclic stress ratio (CSR). Regression relationships between CSR and Tm are proposed for a series of levee types and shaking intensities.

scholarly journals A Comparison of Estimating Crop Residue Cover from Sentinel-2 Data Using Empirical Regressions and Machine Learning Methods

2020 ◽  
Vol 12 (9) ◽  
pp. 1470 ◽  
Author(s):  
Yanling Ding ◽  
Hongyan Zhang ◽  
Zhongqiang Wang ◽  
Qiaoyun Xie ◽  
Yeqiao Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Crop Residue ◽  
Gaussian Process Regression ◽  
Systematic Evaluation ◽  
Support Vector ◽  
Textural Features ◽  
Least Squares Regression ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Sentinel 2

Quantifying crop residue cover (CRC) on field surfaces is important for monitoring the tillage intensity and promoting sustainable management. Remote-sensing-based techniques have proven practical for determining CRC, however, the methods used are primarily limited to empirical regression based on crop residue indices (CRIs). This study provides a systematic evaluation of empirical regressions and machine learning (ML) algorithms based on their ability to estimate CRC using Sentinel-2 Multispectral Instrument (MSI) data. Unmanned aerial vehicle orthomosaics were used to extracted ground CRC for training Sentinel-2 data-based CRC models. For empirical regression, nine MSI bands, 10 published CRIs, three proposed CRIs, and four mean textural features were evaluated using univariate linear regression. The best performance was obtained by a three-band index calculated using (B2 − B4)/(B2 − B12), with an R2cv of 0.63 and RMSEcv of 6.509%, using a 10-fold cross-validation. The methodologies of partial least squares regression (PLSR), artificial neural network (ANN), Gaussian process regression (GPR), support vector regression (SVR), and random forest (RF) were compared with four groups of predictors, including nine MSI bands, 13 CRIs, a combination of MSI bands and mean textural features, and a combination of CRIs and textural features. In general, ML approaches achieved high accuracy. A PLSR model with 13 CRIs and textural features resulted in an accuracy of R2cv = 0.66 and RMSEcv = 6.427%. An RF model with predictors of MSI bands and textural features estimated CRC with an R2cv = 0.61 and RMSEcv = 6.415%. The estimation was improved by an SVR model with the same input predictors (R2cv = 0.67, RMSEcv = 6.343%), followed by a GPR model based on CRIs and textural features. The performance of GPR models was further improved by optimal input variables. A GPR model with six input variables, three MSI bands and three textural features, performed the best, with R2cv = 0.69 and RMSEcv = 6.149%. This study provides a reference for estimating CRC from Sentinel-2 imagery using ML approaches. The GPR approach is recommended. A combination of spectral information and textural features leads to an improvement in the retrieval of CRC.

Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

2018 ◽  
Vol 1 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Chunxiang Qian ◽  
Wence Kang ◽  
Hao Ling ◽  
Hua Dong ◽  
Chengyao Liang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Cross Validation ◽  
Concrete Strength ◽  
Simulation Method ◽  
Support Vector ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Influence Degree ◽  
Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

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.

Sign in / Sign up

Export Citation Format

Share Document