Analysis of international debt problem using artificial neural networks and statistical methods

Yazıcı, Berna; Memmedli, Memmedaga; Aslanargun, Atilla; Asma, Senay

doi:10.1007/s00521-010-0422-4

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…GPR can be a valuable method to dynamically perform inference where it defines the maximum possible output based on given data sets and performs precise function approximation in highdimensional space [30][31][32][33][34][35]. GPR is a non-parametric Bayesian approach to the issue of regression by using Bayesian inference to capture a wide spectrum of relations between input and output [36][37][38][39][40][41][42][43][44][45][46][47][48].…”

Section: Gaussian Process Regression (Gpr)mentioning

confidence: 99%

Application of Gaussian Process Regression (GPR) in Gas Hydrate Mitigation

Suresh

Qasim

Lal

et al. 2021

ARFMTS

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The production of oil and natural gas contributes to a significant amount of revenue generation in Malaysia thereby strengthening the country’s economy. The flow assurance industry is faced with impediments during smooth operation of the transmission pipeline in which gas hydrate formation is the most important. It affects the normal operation of the pipeline by plugging it. Under high pressure and low temperature conditions, gas hydrate is a crystalline structure consisting of a network of hydrogen bonds between host molecules of water and guest molecules of the incoming gases. Industry uses different types of chemical inhibitors in pipeline to suppress hydrate formation. To overcome this problem, machine learning algorithm has been introduced as part of risk management strategies. The objective of this paper is to utilize Machine Learning (ML) model which is Gaussian Process Regression (GPR). GPR is a new approach being applied to mitigate the growth of gas hydrate. The input parameters used are concentration and pressure of Carbon Dioxide (CO2) and Methane (CH4) gas hydrates whereas the output parameter is the Average Depression Temperature (ADT). The values for the parameter are taken from available data sets that enable GPR to predict the results accurately in terms of Coefficient of Determination, R2 and Mean Squared Error, MSE. The outcome from the research showed that GPR model provided with highest R2 value for training and testing data of 97.25% and 96.71%, respectively. MSE value for GPR was also found to be lowest for training and testing data of 0.019 and 0.023, respectively.

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Section: Gaussian Process Regression (Gpr)mentioning

confidence: 99%

Application of Gaussian Process Regression (GPR) in Gas Hydrate Mitigation

Suresh

Qasim

Lal

et al. 2021

ARFMTS

View full text Add to dashboard Cite

show abstract

“…Among the emerging scientific methods for data analysis, computational intelligence methods such as evolutionary algorithm, in addition to artificial neural network, find applications in solving a variety of engineering problems, including the problem of detecting or identifying seismic damage in various engineering structures [12][13][14][15][16][17][18][19] . It is also possible to use hybrid approaches-genetic algorithm and neural network-to develop 3 better performing neural network models for PGA predictions 20 .…”

Section: Introductionmentioning

confidence: 99%

Neural Network Approach for Analyzing Seismic Data to Identify Potentially Hazardous Bridges

Kerh

Huang

Gunaratnam

2011

Mathematical Problems in Engineering

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Examining the effect of strong ground motions on civil engineering structures is important as it concerns public safety. The present study initially selects twenty-one bridges with lengths over 500 m in the Formosa freeway of Taiwan and collects a series of recorded seismic data from checking stations near these bridges. Then, three seismic parameters including focal depth, epicenter distance, and local magnitude are used as the input data sets, and a model for estimating the key seismic parameter-peak ground acceleration-for each of bridge site is developed by using the neural network approach. This model is finally combined with a simple distribution method and a new weight-based method to estimate peak ground acceleration at each of the bridges along the freeway. Based on the seismic design value in the current building code as the evaluation criteria, the model identifies five bridges, out of all the bridges investigated, as having the potential to be subjected to significantly higher horizontal peak ground accelerations than that recommended for design in the building code. The method presented in this study hence provides a valuable reference for dealing with nonlinear seismic data by developing neural network model, and the approach presented is also applicable to other areas of interest around the world.

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A hybrid procedure with feature selection for resolving stock/futures price forecasting problems

Hsu

2011

Neural Comput & Applic

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Analysis of international debt problem using artificial neural networks and statistical methods

Cited by 4 publications

References 26 publications

Application of Gaussian Process Regression (GPR) in Gas Hydrate Mitigation

Application of Gaussian Process Regression (GPR) in Gas Hydrate Mitigation

Neural Network Approach for Analyzing Seismic Data to Identify Potentially Hazardous Bridges

A hybrid procedure with feature selection for resolving stock/futures price forecasting problems

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