Lateral Displacement of Liquefaction Induced Ground Using Least Square Support Vector Machine

2011 ◽  
Vol 2 (2) ◽  
pp. 29-39 ◽  
Author(s):  
Sarat Kumar Das ◽  
Pijush Samui ◽  
Dookie Kim ◽  
N. Sivakugan ◽  
Rajanikanta Biswal
Keyword(s):  
Support Vector Machine ◽  
Model Equation ◽  
Lateral Displacement ◽  
Least Square ◽  
Disaster Mitigation ◽  
Support Vector ◽  
Model Parameters ◽  
Ann Models ◽  
Artificial Neural Network Ann

The determination of lateral displacement of liquefaction induced ground during an earthquake is an imperative task in disaster mitigation. This study investigates the possibility of using least square support vector machine (LSSVM) for the prediction of lateral displacement of liquefaction induced ground during an earthquake. The results have been compared with those obtained using artificial neural network (ANN) models and observed that LSSVM outperformed the ANN models. Model equation has been presented based on the model parameters, which can be used by the professionals. Sensitivity analysis has also been performed to determine the importance of each of the input parameters.

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.

scholarly journals Research on Monthly Precipitation Prediction Based on the Least Square Support Vector Machine with Multi-Factor Integration

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1076
Author(s):  
Jingchun Lei ◽  
Quan Quan ◽  
Pingzhi Li ◽  
Denghua Yan
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Flood Control ◽  
Historical Data ◽  
Yellow River ◽  
Least Square ◽  
Disaster Mitigation ◽  
Support Vector ◽  
Monthly Precipitation ◽  
Precipitation Prediction

Accurate precipitation prediction is of great significance for regional flood control and disaster mitigation. This study introduced a prediction model based on the least square support vector machine (LSSVM) optimized by the genetic algorithm (GA). The model was used to estimate the precipitation of each meteorological station over the source region of the Yellow River (SRYE) in China for 12 months. The Ensemble empirical mode decomposition (EEMD) method was used to select meteorological factors and realize precipitation prediction, without dependence on historical data as a training set. The prediction results were compared with each other, according to the determination coefficient (R2), mean absolute errors (MAE), and root mean square error (RMSE). The results show that sea surface temperature (SST) in the Niño 1 + 2 region exerts the largest influence on accuracy of the prediction model for precipitation in the SRYE (RSST2= 0.856, RMSESST= 19.648, MAESST= 14.363). It is followed by the potential energy of gravity waves (Ep) and temperature (T) that have similar effects on precipitation prediction. The prediction accuracy is sensitive to altitude influences and accurate prediction results are easily obtained at high altitudes. This model provides a new and reliable research method for precipitation prediction in regions without historical data.

State-of-the-Art Least Square Support Vector Machine Application for Accurate Determination of Natural Gas Viscosity

2013 ◽  
Vol 53 (2) ◽  
pp. 945-958 ◽  
Author(s):  
Amir Fayazi ◽  
Milad Arabloo ◽  
Amin Shokrollahi ◽  
Mohammad Hadi Zargari ◽  
Mohammad Hossein Ghazanfari
Keyword(s):  
Support Vector Machine ◽  
Natural Gas ◽  
State Of The Art ◽  
Accurate Determination ◽  
Least Square ◽  
Support Vector ◽  
Gas Viscosity

scholarly journals Modern Soft-Sensing Modeling Methods for Fermentation Processes

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1771 ◽  
Author(s):  
Xianglin Zhu ◽  
Khalil Ur Rehman ◽  
Bo Wang ◽  
Muhammad Shahzad
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Fermentation Process ◽  
Optimization Techniques ◽  
Least Square ◽  
Data Driven ◽  
Support Vector ◽  
Model Parameters ◽  
Soft Sensing ◽  
Modeling Methods

For effective monitoring and control of the fermentation process, an accurate real-time measurement of important variables is necessary. These variables are very hard to measure in real-time due to constraints such as the time-varying, nonlinearity, strong coupling, and complex mechanism of the fermentation process. Constructing soft sensors with outstanding performance and robustness has become a core issue in industrial procedures. In this paper, a comprehensive review of existing data pre-processing approaches, variable selection methods, data-driven (black-box) soft-sensing modeling methods and optimization techniques was carried out. The data-driven methods used for the soft-sensing modeling such as support vector machine, multiple least square support vector machine, neural network, deep learning, fuzzy logic, probabilistic latent variable models are reviewed in detail. The optimization techniques used for the estimation of model parameters such as particle swarm optimization algorithm, ant colony optimization, artificial bee colony, cuckoo search algorithm, and genetic algorithm, are also discussed. A comprehensive analysis of various soft-sensing models is presented in tabular form which highlights the important methods used in the field of fermentation. More than 70 research publications on soft-sensing modeling methods for the estimation of variables have been examined and listed for quick reference. This review paper may be regarded as a useful source as a reference point for researchers to explore the opportunities for further enhancement in the field of soft-sensing modeling.

scholarly journals PREDICTING PRODUCTIVITY LOSS CAUSED BY CHANGE ORDERS USING THE EVOLUTIONARY FUZZY SUPPORT VECTOR MACHINE INFERENCE MODEL

2015 ◽  
Vol 21 (7) ◽  
pp. 881-892 ◽  
Author(s):  
Min-Yuan Cheng ◽  
Dedy Kurniawan Wibowo ◽  
Doddy Prayogo ◽  
Andreas F. V. Roy
Keyword(s):  
Support Vector Machine ◽  
Construction Projects ◽  
Productivity Loss ◽  
Support Vector ◽  
Model Parameters ◽  
Inference Model ◽  
Fuzzy Support Vector Machine ◽  
Change Orders ◽  
Artificial Neural Network Ann ◽  
The Impact

Change orders in construction projects are very common and result in negative impacts on various project facets. The impact of change orders on labor productivity is particularly difficult to quantify. Traditional approaches are inadequate to calculate the complex input-output relationship necessary to measure the effect of change orders. This study develops the Evolutionary Fuzzy Support Vector Machines Inference Model (EFSIM) to more accurately predict change-order-related productivity losses. The EFSIM is an AI-based tool that combines fuzzy logic (FL), support vector machine (SVM), and fast messy genetic algorithm (fmGA). The SVM is utilized as a supervised learning technique to solve classification and regression problems; the FL is used to quantify vagueness and uncertainty; and the fmGA is applied to optimize model parameters. A case study is presented to demonstrate and validate EFSIM performance. Simulation results and our validation against previous studies demonstrate that the EFSIM predicts the impact of change orders significantly better than other AI-based tools including the artificial neural network (ANN), support vector machine (SVM), and evolutionary support vector machine inference model (ESIM).

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.

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