Filters

2024 - 20253
Reset filters

Settings

Institution Author: Yerlikaya Özkurt, Fatma

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Conference Object
    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, Kubilay; Askan, Aysegul; Yerlikaya-Ozkurt, Fatma
    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 Turkiye database are performed to identify MMI maps of the 6th of February 2023, Kahramanmaras 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.
  • Thumbnail Image
    Conference Object
    Evaluation of Multivariate Adaptive Regression Splines for Prediction of Kappa Factor in Western Turkiye
    (Springer international Publishing Ag, 2024) Kurtulmus, T. O.; Yerlikaya-Ozkurt, F.; Askan, A.
    The recent seismic activity on the west coast of Turkiye, including the Aegean Sea region, indicates that a closer focus is necessary on this region. Located in an active tectonic regime of north-south extension with multiple basins on soft soil deposits, the region has a high seismic hazard. Recently, as a combination of basin effects and building vulnerability, the October 30, 2020, Samos event (Mw = 7.0) caused localized significant damage and collapse in Izmir city center despite the 70 km distance from the earthquake source. In spite of this activity, studies on site characterization and site response modeling, including local velocity models and kappa estimates, are still limited in this region. Kappa values exhibit regional characteristics, which necessitates local kappa estimates from past earthquake data for use in region-specific applications. To make the prediction, we used three-component strong ground motion records from accelerometer stations with known VS30 values in western Turkiye that are a part of the Disaster and Emergency Management Presidency's Turkish National Strong Ground Motion Observation Network. Multiple linear regression (MLR) and multivariate adaptive regression splines (MARS) algorithms have been implemented to build the prediction model. Three factors, such as distance, magnitude, and site class, are included in the kappa evaluation process. The performance of the models in kappa evaluation is calculated based on wellknown accuracy measures. The MARS model showed better performance compared to MLR over the selected sites concerning all performance measures. This finding may challenge the most commonly assumed linear models of kappa in the literature.
  • Thumbnail Image
    Article
    Citation - WoS: 8
    Citation - Scopus: 8
    Modeling the Mood State on Thermal Sensation With a Data Mining Algorithm and Testing the Accuracy of Mood State Correction Factor
    (Pergamon-elsevier Science Ltd, 2025) Yerlikaya-Ozkurt, Fatma; Ozbey, Mehmet Furkan; Turhan, Cihan
    Psychology is proven as an influencing factor on thermal sensation. On the other hand, mood state is one of the significant parameters in psychology field. To this aim, in the literature, mood state correction factor on thermal sensation (Turhan and Ozbey coefficients) is derived utilizing with data-driven black-box model. However, novel models which present analytical form of the mood state correction factor should be derived based on the several descriptive variables on thermal sensation. Moreover, the result of this factor should also be checked with analytical model results. Therefore, this study investigates the modelling of mood state correction factor with a data mining algorithm, called Multivariate Adaptive Regression Splines (MARS). Additionally, the mood state is also taken as a thermal sensation parameter besides environmental parameters in this algorithm. The same data, which are collected from a university study hall in a temperate climate zone, are used and the model results are compared with the thermal sensation results based on mood state correction factor which is driven via black-box model. The results show that coefficient of correlation "r" between the MARS and black-box model is found as 0.9426 and 0.9420 for training and testing. Hence, the mood state is also modelled via a data mining algorithm with a high accuracy, besides the black-box model.