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            <front>

                <journal-meta>
                                    <journal-id></journal-id>
            <journal-title-group>
                                                                                    <journal-title>Mugla Journal of Science and Technology</journal-title>
            </journal-title-group>
                            <issn pub-type="ppub">2149-3596</issn>
                                                                                                        <publisher>
                    <publisher-name>Mugla Sitki Kocman University</publisher-name>
                </publisher>
                    </journal-meta>
                <article-meta>
                                        <article-id pub-id-type="doi">10.22531/muglajsci.1602580</article-id>
                                                                <article-categories>
                                            <subj-group  xml:lang="en">
                                                            <subject>Civil Geotechnical Engineering</subject>
                                                            <subject>Soil Mechanics in Civil Engineering</subject>
                                                            <subject>Geology of Engineering</subject>
                                                            <subject>Applied Geology</subject>
                                                    </subj-group>
                                            <subj-group  xml:lang="tr">
                                                            <subject>İnşaat Geoteknik Mühendisliği</subject>
                                                            <subject>İnşaat Mühendisliğinde Zemin Mekaniği</subject>
                                                            <subject>Mühendislik Jeolojisi</subject>
                                                            <subject>Uygulamalı Jeoloji</subject>
                                                    </subj-group>
                                    </article-categories>
                                                                                                                                                        <title-group>
                                                                                                                        <article-title>ESTIMATION OF SOIL LIQUEFACTION POTENTIAL IN THE DALAMAN RESIDENTIAL AREA USING A SUPERVISED MACHINE LEARNING MODEL</article-title>
                                                                                                                                                                                                <trans-title-group xml:lang="tr">
                                    <trans-title>DALAMAN YERLEŞİM ALANINDAKİ TOPRAK ZEMİNLERİN SIVILAŞMA POTANSİYELİNİN DENETİMLİ MAKİNE ÖĞRENMESİ MODELİ İLE TAHMİNİ</trans-title>
                                </trans-title-group>
                                                                                                    </title-group>
            
                                                    <contrib-group content-type="authors">
                                                                        <contrib contrib-type="author">
                                                                    <contrib-id contrib-id-type="orcid">
                                        https://orcid.org/0000-0002-7708-3903</contrib-id>
                                                                <name>
                                    <surname>Türe</surname>
                                    <given-names>Orkun</given-names>
                                </name>
                                                                    <aff>MUGLA SITKI KOCMAN UNIVERSITY, FACULTY OF ENGINEERING, DEPARTMENT OF GEOLOGICAL ENGINEERING</aff>
                                                            </contrib>
                                                    <contrib contrib-type="author">
                                                                    <contrib-id contrib-id-type="orcid">
                                        https://orcid.org/0000-0002-6583-4861</contrib-id>
                                                                <name>
                                    <surname>Karacan</surname>
                                    <given-names>Ergun</given-names>
                                </name>
                                                                    <aff>MUGLA SITKI KOCMAN UNIVERSITY, FACULTY OF ENGINEERING, DEPARTMENT OF GEOLOGICAL ENGINEERING</aff>
                                                            </contrib>
                                                                                </contrib-group>
                        
                                        <pub-date pub-type="pub" iso-8601-date="20250630">
                    <day>06</day>
                    <month>30</month>
                    <year>2025</year>
                </pub-date>
                                        <volume>11</volume>
                                        <issue>1</issue>
                                        <fpage>28</fpage>
                                        <lpage>36</lpage>
                        
                        <history>
                                    <date date-type="received" iso-8601-date="20241217">
                        <day>12</day>
                        <month>17</month>
                        <year>2024</year>
                    </date>
                                                    <date date-type="accepted" iso-8601-date="20250411">
                        <day>04</day>
                        <month>11</month>
                        <year>2025</year>
                    </date>
                            </history>
                                        <permissions>
                    <copyright-statement>Copyright © 2015, Mugla Journal of Science and Technology</copyright-statement>
                    <copyright-year>2015</copyright-year>
                    <copyright-holder>Mugla Journal of Science and Technology</copyright-holder>
                </permissions>
            
                                                                                                <abstract><p>Liquefaction is a critical phenomenon in geotechnical engineering, especially in mixed depositional environments where different soil types coexist. In such environments, assessment of liquefaction potential may be challenging. However, machine learning techniques overcome these challenges. In this study, to estimate the liquefaction potentials of the soils in the Dalaman residential area which is situated in a mixed depositional environment, supervised Multilayer Perceptron (MLP) model has been generated by using seismic parameters from the Chi-Chi and Kocaeli earthquakes and the parameters of the soils affected by these earthquakes. Sensitivity, specificity, accuracy, precision F1 score and AUC have been calculated for training and testing phases in model generation stage and for Dalaman Region. These values have been found to be 77.9%, 91.5, 85.7%, 87.0%, 0.822 and 0.930 in training phase; 80%, 83.1%, 81.8%, 75%, 0.774 and 0.930 in testing phase. For Dalaman residential area, these values have been found as 81.3%, 86.2%, 83.5%, 87.25% and 0.841. When the values from training and testing phase are compared to the results of Dalaman Region, it can be said that the model accurately estimated the liquefaction potentials of the soils in the Dalaman residential area.</p></abstract>
                                                                                                                                    <trans-abstract xml:lang="tr">
                            <p>Sıvılaşma, özellikle karmaşık zemin tiplerinin bir arada bulunduğu karmaşık çökelim ortamlarında, geoteknik mühendisliği açısından kritik bir olgudur. Bu tür ortamlarda sıvılaşma potansiyelinin değerlendirilmesi zorlayıcı olabilir ancak makine öğrenimi teknikleri kullanarak, bu zorluklar kolayca aşılabilir. Bu çalışmada, karışık çökelim ortamında bulunan Dalaman yerleşim alanındaki zeminlerin sıvılaşma potansiyellerini tahmin etmek amacıyla, Chi-Chi ve Kocaeli depremlerine ait sismik parametreler ile bu depremlerden etkilenen zeminlerin parametreleri kullanılarak, denetimli bir makine öğrenimi algoritması olan Çok Katmanlı Algılayıcı (MLP) modeli geliştirilmiştir. Modelin oluşturulma aşamasındaki duyarlılık, özgüllük, doğruluk, kesinlik, F1-değeri ve AUC değeri eğitim aşamasında %77,9, %91,5, %85,7, %87,0, 0.822 ve 0.930 olarak, test aşamasında ise %80, %83,1, %81,8, %75, 0.774 ve 0.930 olarak bulunmuştur. Oluşturulan modelin Dalaman yerleşim alanındaki performans ölçütleri ise %81,3, %86,2, %83,5, %87,25 ve 0.841 olarak bulunmuştur. Bu değerler kıyaslandığında oluşturulan modelin Dalaman bölgesindeki zeminlerin sıvılaşma potansiyelini başarılı bir şekilde tahmin ettiği görülmüştür.</p></trans-abstract>
                                                            
            
                                                            <kwd-group>
                                                    <kwd>Liquefaction</kwd>
                                                    <kwd>  Multilayer Perceptron Machine Learning Technique</kwd>
                                                    <kwd>  Mixed Depositional Environment</kwd>
                                                    <kwd>  Dalaman</kwd>
                                            </kwd-group>
                                                        
                                                                            <kwd-group xml:lang="tr">
                                                    <kwd>Sıvılaşma</kwd>
                                                    <kwd>  Çok Katmanlı Algılayıcı Makine Öğrenimi Tekniği</kwd>
                                                    <kwd>  Karışık Çökelim Ortamı</kwd>
                                                    <kwd>  Dalaman</kwd>
                                            </kwd-group>
                                                                                                        <funding-group specific-use="FundRef">
                    <award-group>
                                                    <funding-source>
                                <named-content content-type="funder_name">No grants or funds were recieved for this study</named-content>
                            </funding-source>
                                                                    </award-group>
                </funding-group>
                                </article-meta>
    </front>
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