Fatma Yerlikaya Özkurt

Yerlikaya Özkurt, Fatma

Name Variants

Yerlikaya-Özkurt,F.
F., Yerlikaya Ozkurt
Y.,Fatma
Y., Fatma
Yerlikaya Özkurt,F.
Fatma Yerlikaya Özkurt
Yerlikaya Ozkurt,Fatma
Yerlikaya Ozkurt,F.
F., Yerlikaya Özkurt
Yerlikaya Ozkurt, Fatma
Fatma, Yerlikaya Özkurt
F.,Yerlikaya Özkurt
Y.Ö.Fatma
F.,Yerlikaya Ozkurt
Fatma, Yerlikaya Ozkurt
Y. Ö. Fatma
Y. O. Fatma
Yerlikaya Özkurt, Fatma
Yerlikaya-Ozkurt, Fatma
Yerlikaya-Ozkurt, Fatma
Yerlikaya-oezkurt, Fatma
Yerlikaya-Ozkurt,F.
Özkurt, Fatma Yerlikaya
Ozkurt, F. Yerlikaya

Job Title

Doktor Öğretim Üyesi

Email Address

fatma.yerlikaya@atilim.edu.tr

ORCID ID

0000-0002-4747-8461

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Scholarly Output

15

Articles

12

Citation Count

33

Supervised Theses

1 Scholarly Output Search Results

Now showing 1 - 10 of 15

Modeling of Kappa Factor Using Multivariate Adaptive Regression Splines: Application To the Western Türkiye Ground Motion Dataset
(Springer, 2024) Kurtulmus, Tevfik Ozgur; Yerlikaya-Ozkurt, Fatma; Askan, Aysegul; Industrial Engineering
The recent seismic activity on Turkiye's west coast, especially in the Aegean Sea region, shows that this region requires further attention. The region has significant seismic hazards because of its location in an active tectonic regime of North-South extension with multiple basin structures on soft soil deposits. Recently, despite being 70 km from the earthquake source, the Samos event (with a moment magnitude of 7.0 on October 30, 2020) caused significant localized damage and collapse in the Izmir city center due to a combination of basin effects and structural susceptibility. Despite this activity, research on site characterization and site response modeling, such as local velocity models and kappa estimates, remains sparse in this region. Kappa values display regional characteristics, necessitating the use of local kappa estimations from previous earthquake data in region-specific applications. Kappa estimates are multivariate and incorporate several characteristics such as magnitude and distance. In this study, we assess and predict the trend in mean kappa values using three-component strong-ground motion data from accelerometer sites with known VS30 values throughout western Turkiye. Multiple linear regression (MLR) and multivariate adaptive regression splines (MARS) were used to build the prediction models. The effects of epicentral distance Repi, magnitude Mw, and site class (VS30) were investigated, and the contributions of each parameter were examined using a large dataset containing recent seismic activity. The models were evaluated using well-known statistical accuracy criteria for kappa assessment. In all performance measures, the MARS model outperforms the MLR model across the selected sites.
A new approach to adaptive spline threshold autoregression by using Tikhonov regularization and continuous optimization
(Taru Publications, 2019) Yalaz, S.; Taylan, P.; Ozkurt, F. Yerlikaya; Industrial Engineering
In this study adaptive spline threshold autoregression and conic quadratic programming is used to develope conic adaptive spline threshold autoregression. With the introduced approach the second stepwise algorithm of adaptive spline threshold autoregression model turned to the Tikhonov regularization problem which was transformed into conic quadratic programming problem. The aim is to attain an optimum solution chosen in many solutions obtained by determining the bounds of the optimization problem using multiobjective optimization approach. Furthermore, in application part we used two different data set to compare performances of linear regression, adaptive spline threshold autoregression and conic adaptive spline threshold autoregression approaches.
Comparison of Field Survey-Based Macroseismic Intensity Map and Numerical Macroseismic Intensity Maps Using Mars and Multiple Regression Methods for 6th of February 2023 Kahramanmaraş Earthquakes
(Springer Science and Business Media Deutschland GmbH, 2024) Albayrak,K.; Askan,A.; Yerlikaya-Özkurt,F.; Industrial Engineering
Earthquakes are natural events that cause damage to built environments by the instant seismic energy release. This energy is measured by instrumental devices to obtain the peak ground motion parameters such as Peak Ground Acceleration (PGA) and Peak Ground Velocity (PGV). Additional measurements based on the questionnaires after the earthquakes are required to identify the felt or macro seismic intensity levels. These measurements are crucial to identify the total effects of earthquakes over not only an area but also for the spatial distribution of ground motion parameters. For this purpose, it is important to study the multi-variable criteria correlations between ground motion parameters and Modified Mercalli Intensity (MMI) levels based on linear relationships of predictor variable couples. In this regard, the Multivariate Adaptive Regression Splines (MARS) Method and the Multiple Linear Regression Method are used. The entire dataset is composed of 69 earthquakes between 2005 and 2022 with 2171 ground motion parameters coupled with MMI levels. For MMI-based correlations, the MARS method is used to identify the non-linearities between predictor variables by piecewise linear functions, but for the Multiple Linear Regression Method, the least correlated variables of PGA-Epicentral Distance and PGV-Epicentral Distance are used to obtain the relationship between MMI and PGM parameters. The resulting equations obtained for the entire Türkiye database are performed to identify MMI maps of the 6th of February 2023, Kahramanmaraş Earthquakes, and these maps are used to check the accuracy of the results by the comparison of field survey-based MMI maps. Finally, the numerical MMI maps are found to be consistent with the field survey-based MMI maps. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Spline Based Sparseness and Smoothness for Partially Nonlinear Model Via C-Fused Lasso
(American Institute of Mathematical Sciences, 2025) Taylan, P.; Yerlikaya-¨Ozkurt, F.; Tez, M.; Industrial Engineering
One of the most beneficial and widely used models for data analysis are partially nonlinear models (PNLRM), which consists of parametric and nonparametric components. Since the model includes the coefficients of both the parametric and nonparametric parts, the complexity of the model will be high and its interpretation will be very difficult. In this study, we propose a procedure that not only achieves sparseness, but also smoothness for PNLRM to obtain a simpler model that better explains the relationship between the response and covariates. Thus, the fused Lasso problem is taken into account where nonparametric components are expressed as a spline basis function, and then the Fused Lasso estimation problem is built and expressed in terms of conic quadratic programming. Applications are conducted to evaluate the performance of the proposed method by considering commonly utilized measures. Promising results are obtained, especially in the data with nonlinearly correlated variables. © (2025), (American Institute of Mathematical Sciences). All rights reserved.
Cmars: a Powerful Predictive Data Mining Package in R
(Elsevier, 2023) Yerlikaya-oezkurt, Fatma; Yazici, Ceyda; Batmaz, Inci; Industrial Engineering
Conic Multivariate Adaptive Regression Splines (CMARS) is a very successful method for modeling nonlinear structures in high-dimensional data. It is based on MARS algorithm and utilizes Tikhonov regularization and Conic Quadratic Optimization (CQO). In this paper, the open-source R package, cmaRs, built to construct CMARS models for prediction and binary classification is presented with illustrative applications. Also, the CMARS algorithm is provided in both pseudo and R code. Note here that cmaRs package provides a good example for a challenging implementation of CQO based on MOSEK solver in R environment by linking R MOSEK through the package Rmosek.
Strategic Electricity Production Planning of Turkey Via Mixed Integer Programming Based on Time Series Forecasting
(Mdpi, 2023) Yoruk, Gokay; Bac, Ugur; Yerlikaya-Ozkurt, Fatma; Unlu, Kamil Demirberk; Industrial Engineering
This study examines Turkey's energy planning in terms of strategic planning, energy policy, electricity production planning, technology selection, and environmental policies. A mixed integer optimization model is proposed for strategic electricity planning in Turkey. A set of energy resources is considered simultaneously in this research, and in addition to cost minimization, different strategic level policies, such as CO2 emission reduction policies, energy resource import/export restriction policies, and renewable energy promotion policies, are also considered. To forecast electricity demand over the planning horizon, a variety of forecasting techniques, including regression methods, exponential smoothing, Winter's method, and Autoregressive Integrated Moving Average methods, are used, and the best method is chosen using various error measures. The optimization model constructed for Turkey's Strategic Electricity Planning is obtained for two different planning intervals. The findings indicate that the use of renewable energy generation options, such as solar, wind, and hydroelectric alternatives, will increase significantly, while the use of fossil fuels in energy generation will decrease sharply. The findings of this study suggest a gradual increase in investments in renewable energy-based electricity production strategies are required to eventually replace fossil fuel alternatives. This change not only reduces investment, operation, and maintenance costs, but also reduces emissions in the long term.
Estimation in the Partially Nonlinear Model by Continuous Optimization
(Taylor & Francis Ltd, 2021) Yerlikaya-Ozkurt, Fatma; Taylan, Pakize; Tez, Mujgan; Industrial Engineering
A useful model for data analysis is the partially nonlinear model where response variable is represented as the sum of a nonparametric and a parametric component. In this study, we propose a new procedure for estimating the parameters in the partially nonlinear models. Therefore, we consider penalized profile nonlinear least square problem where nonparametric components are expressed as a B-spline basis function, and then estimation problem is expressed in terms of conic quadratic programming which is a continuous optimization problem and solved interior point method. An application study is conducted to evaluate the performance of the proposed method by considering some well-known performance measures. The results are compared against parametric nonlinear model.
A New Outlier Detection Method Based on Convex Optimization: Application To Diagnosis of Parkinson's Disease
(Taylor & Francis Ltd, 2021) Taylan, Pakize; Yerlikaya-Ozkurt, Fatma; Bilgic Ucak, Burcu; Weber, Gerhard-Wilhelm; Department of Electrical & Electronics Engineering; Industrial Engineering
Neuroscience is a combination of different scientific disciplines which investigate the nervous system for understanding of the biological basis. Recently, applications to the diagnosis of neurodegenerative diseases like Parkinson's disease have become very promising by considering different statistical regression models. However, well-known statistical regression models may give misleading results for the diagnosis of the neurodegenerative diseases when experimental data contain outlier observations that lie an abnormal distance from the other observation. The main achievements of this study consist of a novel mathematics-supported approach beside statistical regression models to identify and treat the outlier observations without direct elimination for a great and emerging challenge in humankind, such as neurodegenerative diseases. By this approach, a new method named as CMTMSOM is proposed with the contributions of the powerful convex and continuous optimization techniques referred to as conic quadratic programing. This method, based on the mean-shift outlier regression model, is developed by combining robustness of M-estimation and stability of Tikhonov regularization. We apply our method and other parametric models on Parkinson telemonitoring dataset which is a real-world dataset in Neuroscience. Then, we compare these methods by using well-known method-free performance measures. The results indicate that the CMTMSOM method performs better than current parametric models.
Konut Piyasalarında Makroekonomik Faktörlerin Etkisi: Abd Örneği
(2021) Yılmaz, Bilgi; Kestel, A. Sevtap Selçuk; Özkurt, Fatma Yerlikaya; Industrial Engineering
Bu makale makroekonomik, finansal ve emtia piyasaları göstergelerinin konut piyasaları üzerindeki etkisini analiz etmektedir. Genelleştirilmiş Doğrusal Modeller (GDM) ve Çok Değişkenli Uyarlanabilir Regresyon Eğrileri (ÇDRE) tarafından üretilen modellerin yeterliliğini konut piyasası eğilimini tahmin etmek için bağımsız ölçüm yöntemlerine göre karşılaştırıyoruz. Araştırmalarımıza göre bu modeller ilk kez makroekonomik göstergelerin konut piyasaları üzerindeki etkisini ve konut piyasalarındaki eğilimine yönelik tahmini belirlemekte kullanılmaktadır. Ampirik çalışmalar, ABD konut piyasalarına odaklanmakta ve önerilen modellerin gösterimi Ocak 1999-Haziran 2018 periyodu arasında gözlemlenen aylık S\\&P/Case-Shiller Ulusal Konut Fiyat İndeksine ve ABD macroeconomic faktörlerine uygulanmaktadır. Bu çalışma makro ekonomik göstergeler ve konut piyasaları arasındaki etkileşimi vurgulayarak ve konut piyasalarının mekanizmasını analiz ederek literatüre katkıda bulunmaktadır. Bulguları, konut fiyat eğilimlerinin daha doğru bir şekilde tahmin edildiğini ve bu modellerin açıklayıcı değişkenlerin ortak etkisini yakaladığını göstermektedir. Ayrıca, ÇDRE yönteminin tahmin ve geleceğe yönelik tahmin gücüne kıyasla GDM'den daha iyi performans gösterdiğini ortaya koymuştur.
New Computational Methods for Classification Problems in the Existence of Outliers Based on Conic Quadratic Optimization
(Taylor & Francis inc, 2020) Yerlikaya-Ozkurt, Fatma; Taylan, Pakize; Industrial Engineering
Most of the statistical research involves classification which is a procedure utilized to establish prediction models to set apart and classify new observations in the dataset from every fields of science, technology, and economics. However, these models may give misclassification results when dataset contains outliers (extreme data points). Therefore, we dealt with outliers in classification problem: firstly, by combining robustness of mean-shift outlier model and then stability of Tikhonov regularization based on continuous optimization method called Conic Quadratic Programming. These new methodologies are performed on classification dataset within the existence of outliers, and the results are compared with parametric model by using well-known performance measures.

Yerlikaya Özkurt, Fatma

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Scholarly Output

15

Articles

12

Citation Count

33

Supervised Theses

1

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