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.

scholarly journals Evaluating the performance of support vector machines based on different kernel methods for forecasting air pollutants

2020 ◽  
pp. 5-14
Author(s):  
Adven Masih ◽  
Alexander N. Medvedev
Keyword(s):  
Air Pollution ◽  
Support Vector Machines ◽  
Correlation Coefficient ◽  
Air Pollutants ◽  
Prediction Models ◽  
Pollution Monitoring ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Squared Error ◽  
Vector Machines

The alarming level of air pollution in urban centres is an urgent threat to human health. Its consequences can be measured in terms of health issues experienced by children, an increasing numbers of heart and lung diseases, and, most importantly, the number of pollution related deaths. That is why a lot of attention has recently been paid to air pollution monitoring and prediction modelling. In order to develop prediction models, the study uses Support Vector Machines (SVM) with linear, polynomial, radial base function, normalised polynomial, and Pearson VII function kernels to predict the hourly concentration of pollutants in the air. The paper analyses the monitoring dataset of air pollutants and meteorological parameters as input variable to predict the concentrations of various air pollutants. The prediction performance of the models was assessed by using evaluation metrics, namely the correlation coefficient, root mean squared error, relative absolute error, and relative root squared error. To validate the model, the accuracy of the predictive algorithm was tested against two widely and commonly applied regression approaches called multilayer perceptron and linear regression. Furthermore, back check prediction test was performed to examine the consistency of the models. According to the results, the Pearson VII function and normalised polynomial kernel yield the most accurate results in terms of the correlation coefficient and error values to predict the concentrations of atmospheric pollutants as compared to other SVM kernels and traditional prediction models.

An investigation of the factors influencing cost system functionality using decision trees, support vector machines and logistic regression

2019 ◽  
Vol 27 (1) ◽  
pp. 27-55 ◽  
Author(s):  
Cemil Kuzey ◽  
Ali Uyar ◽  
Dursun Delen
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Support Vector Machines ◽  
Decision Trees ◽  
Prediction Models ◽  
Support Vector ◽  
Content Type ◽  
Cost System ◽  
Factors Influencing ◽  
Vector Machines

Purpose The paper aims to identify and critically analyze the factors influencing cost system functionality (CSF) using several machine learning techniques including decision trees, support vector machines and logistic regression. Design/methodology/approach The study used a self-administered survey method to collect the necessary data from companies conducting business in Turkey. Several prediction models are developed and tested; a series of sensitivity analyses is performed on the developed prediction models to assess the ranked importance of factors/variables. Findings Certain factors/variables influence CSF much more than others. The findings of the study suggest that utilization of management accounting practices require a functional cost system, which is supported by a comprehensive cost data management process (i.e. acquisition, storage and utilization). Research limitations/implications The underlying data were collected using a questionnaire survey; thus, it is subjective which reflects the perceptions of the respondents. Ideally, it is expected to reflect the objective of the practices of the firms. Second, the authors have measured CSF it on a “Yes” or “No” basis which does not allow survey respondents reply in between them; thus, it might have limited the choices of the respondents. Third, the Likert scales adopted in the measurement of the other constructs might be limiting the answers of the respondents. Practical implications Information technology plays a very important role for the success of CSF practices. That is, successful implementation of a functional cost system relies heavily on a fully integrated information infrastructure capable of constantly feeding CSF with accurate, relevant and timely data. Originality/value In addition to providing evidence regarding the factors underlying CSF based on a broad range of industries interesting finding, this study also illustrates the viability of machine learning methods as a research framework to critically analyze domain specific data.

Applying Support Vector Machines in Rebound Hammer Test

2013 ◽  
Vol 853 ◽  
pp. 600-604 ◽  
Author(s):  
Yu Ren Wang ◽  
Wen Ten Kuo ◽  
Shian Shien Lu ◽  
Yi Fan Shih ◽  
Shih Shian Wei
Keyword(s):  
Artificial Intelligence ◽  
Compressive Strength ◽  
Support Vector Machines ◽  
Prediction Model ◽  
Prediction Models ◽  
Percentage Error ◽  
Support Vector ◽  
Concrete Compressive Strength ◽  
Rebound Hammer ◽  
Vector Machines

There are several nondestructive testing techniques available to test the compressive strength of the concrete and the Rebound Hammer Test is among one of the fast and economical methods. Nevertheless, it is found that the prediction results from Rebound Hammer Test are not satisfying (over 20% mean absolute percentage error). In view of this, this research intends to develop a concrete compressive strength prediction model for the SilverSchmidt test hammer, using data collected from 838 lab tests. The Q-values yield from the concrete test hammer SilverSchmidt is set as the input variable and the concrete compressive strength is set as the output variable for the prediction model. For the non-linear relationships, artificial intelligence technique, Support Vector Machines (SVMs), are adopted to develop the prediction models. The results show that the mean absolute percentage errors for SVMs prediction model, 6.76%, improves a lot when comparing to SilverSchmidt predictions. It is recommended that the artificial intelligence prediction models can be applied in the SilverSchmidt tests to improve the prediction accuracy.

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