A genetic algorithm-based support vector machine to estimate the transverse mixing coefficient in streams

Abstract Transverse mixing coefficient (TMC) is known as one of the most effective parameters in the two-dimensional simulation of water pollution, and increasing the accuracy of estimating this coefficient will improve the modeling process. In the present study, genetic algorithm (GA)-based support vector machine (SVM) was used to estimate TMC in streams. There are three principal parameters in SVM which need to be adjusted during the estimating procedure. GA helps SVM and optimizes these three parameters automatically in the best way. The accuracy of the SVM and GA-SVM algorithms along with previous models were discussed in TMC estimation by using a wide range of hydraulic and geometrical data from field and laboratory experiments. According to statistical analysis, the performance of the mentioned models in both straight and meandering streams was more accurate than the regression-based models. Sensitivity analysis showed that the accuracy of the GA-SVM algorithm in TMC estimation significantly correlated with the number of input parameters. Eliminating the uncorrelated parameters and reducing the number of input parameters will reduce the complexity of the problem and improve the TMC estimation by GA-SVM.

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Estimation of CO2 Diffusivity in Brine by Use of the Genetic Algorithm and Mixed Kernels-Based Support Vector Machine Model

Journal of Energy Resources Technology ◽

10.1115/1.4041724 ◽

2018 ◽

Vol 141 (4) ◽

Cited By ~ 4

Author(s):

Qihong Feng ◽

Ronghao Cui ◽

Sen Wang ◽

Jin Zhang ◽

Zhe Jiang

Keyword(s):

Genetic Algorithm ◽

Support Vector Machine ◽

Diffusion Coefficient ◽

Co2 Storage ◽

Co2 Injection ◽

Support Vector ◽

Hybrid Technique ◽

Machine Model ◽

Proposed Model ◽

Wide Range

Diffusion coefficient of carbon dioxide (CO2), a significant parameter describing the mass transfer process, exerts a profound influence on the safety of CO2 storage in depleted reservoirs, saline aquifers, and marine ecosystems. However, experimental determination of diffusion coefficient in CO2-brine system is time-consuming and complex because the procedure requires sophisticated laboratory equipment and reasonable interpretation methods. To facilitate the acquisition of more accurate values, an intelligent model, termed MKSVM-GA, is developed using a hybrid technique of support vector machine (SVM), mixed kernels (MK), and genetic algorithm (GA). Confirmed by the statistical evaluation indicators, our proposed model exhibits excellent performance with high accuracy and strong robustness in a wide range of temperatures (273–473.15 K), pressures (0.1–49.3 MPa), and viscosities (0.139–1.950 mPa·s). Our results show that the proposed model is more applicable than the artificial neural network (ANN) model at this sample size, which is superior to four commonly used traditional empirical correlations. The technique presented in this study can provide a fast and precise prediction of CO2 diffusivity in brine at reservoir conditions for the engineering design and the technical risk assessment during the process of CO2 injection.

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Nonlinear correction of sensor using genetic algorithm and support vector machine

JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT ◽

10.3724/sp.j.1187.2011.00056 ◽

2011 ◽

Vol 25 (1) ◽

pp. 56-60 ◽

Cited By ~ 1

Author(s):

Tao Liu ◽

Hua Wang

Keyword(s):

Genetic Algorithm ◽

Support Vector Machine ◽

Support Vector ◽

Nonlinear Correction

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A New Method to Obtain 3-D Surface Deformations From InSAR and GNSS Data With Genetic Algorithm and Support Vector Machine

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2021.3049128 ◽

2021 ◽

pp. 1-5

Author(s):

Panfeng Ji ◽

Xiaolei Lv ◽

Jingchuan Yao ◽

Guangcai Sun

Keyword(s):

Genetic Algorithm ◽

Support Vector Machine ◽

New Method ◽

Support Vector ◽

Surface Deformations ◽

Gnss Data

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Grasp to See—Object Classification Using Flexion Glove with Support Vector Machine

Sensors ◽

10.3390/s21041461 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1461

Author(s):

Shun-Hsin Yu ◽

Jen-Shuo Chang ◽

Chia-Hung Dylan Tsai

Keyword(s):

Support Vector Machine ◽

Daily Life ◽

Object Classification ◽

Target Object ◽

The Other ◽

Support Vector ◽

Wide Range ◽

Dark Space ◽

Flex Sensors ◽

Shape And Size

This paper proposes an object classification method using a flexion glove and machine learning. The classification is performed based on the information obtained from a single grasp on a target object. The flexion glove is developed with five flex sensors mounted on five finger sleeves, and is used for measuring the flexion of individual fingers while grasping an object. Flexion signals are divided into three phases, and they are the phases of picking, holding and releasing, respectively. Grasping features are extracted from the phase of holding for training the support vector machine. Two sets of objects are prepared for the classification test. One is printed-object set and the other is daily-life object set. The printed-object set is for investigating the patterns of grasping with specified shape and size, while the daily-life object set includes nine objects randomly chosen from daily life for demonstrating that the proposed method can be used to identify a wide range of objects. According to the results, the accuracy of the classifications are achieved 95.56% and 88.89% for the sets of printed objects and daily-life objects, respectively. A flexion glove which can perform object classification is successfully developed in this work and is aimed at potential grasp-to-see applications, such as visual impairment aid and recognition in dark space.

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