Developing new VO 2 max prediction models from maximal, submaximal and questionnaire variables using support vector machines combined with feature selection

2016 ◽  
Vol 79 ◽  
pp. 182-192 ◽  
Author(s):  
Fatih Abut ◽  
Mehmet Fatih Akay ◽  
James George
Keyword(s):  
Feature Selection ◽  
Support Vector Machines ◽  
Prediction Models ◽  
Support Vector ◽  
Vector Machines

scholarly journals Study on the Magnitude of Reservoir-Triggered Earthquake Based on Support Vector Machines

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Hai Wei ◽  
Mingming Wang ◽  
Bingyue Song ◽  
Xin Wang ◽  
Danlei Chen
Keyword(s):  
Support Vector Machines ◽  
Prediction Models ◽  
Influence Factors ◽  
Tectonic Stress ◽  
Support Vector ◽  
Fault Features ◽  
Vector Machines ◽  
Triggered Earthquake ◽  
Sample Data ◽  
Hierarchy Process

An effective approach is introduced to predict the magnitude of reservoir-triggered earthquake (RTE), based on support vector machines (SVM) and fuzzy support vector machines (FSVM) methods. The main influence factors on RTE, including lithology, rock mass integrity, fault features, tectonic stress state, and seismic activity background in reservoir area, are categorized into 11 parameters and quantified by using analytical hierarchy process (AHP). Dataset on 100 reservoirs in China, including the 48 well-documented cases of RTE, are collected and used to train and validate the prediction models established with SVM and FSVM, respectively. Through numerical tests, it is found that both the SVM and FSVM models are effective in the prediction of the magnitude of RTE with high accuracy, provided that sufficient samples are collected. While the results of FSVM which is extended from SVM by introducing a fuzzy membership to reduce the influence of noises or outliers are found to be slightly less accurate than those of SVM in the current analysis of RTE cases. The reason might be attributed to the high discreteness of the sample data in the current study.

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