Sıvılaşma Tahmininde Özyinelemeli Özellik Seçmeye Dayalı Faktör Seçme Yönteminin Değerlendirilmesi

Bu çalışma, zemin sıvılaşması tahmini için özyinelemeli özellik seçimi (RFE) ile rastgele orman (RF) algoritması kullanan bir makine öğrenme modeli sunmaktadır. Tahmin modeli, farklı depremlerden elde edilen 253 CPT tabanlı saha verileri üzeri kullanılarak test edilmiştir. Söz konusu veri setindeki ihtiyaç fazlası özelliklerin elimine edilmesi için özellik seçim yöntemlerinden biri olan RFE benimsenmiştir. Ardından RFE-RF'nin (yani RFE yöntemiyle belirlenen modelin) ve bütün değişkenlerin kullanıldığı RF modelin performansları performans matrisleri açısından incelenmiş ve karşılaştırılmıştır. Bu çalışmanın önceliği, öznitelik seçim algoritması yaklaşımının etkinliğini araştırmaktır, bu nedenle RFE-RF'nin performansını karşılaştırmak için bir kıyaslama veri seti olan ham veriler kullanılmıştır. Sonuç olarak, RFE yaklaşımının kullanılmasının sıvılaşma tahmin modelinin genel doğruluğunu arttırdığı görülmüştür.

Anahtar Kelimeler:

Sıvılaşma Tahmini, Özellik Seçimi, Makine Öğrenme, Özyinelemeli Özellik Seçimi, Rastgele Orman

Assessment of Feature Selection for Liquefaction Prediction Based on Recursive Feature Elimination

This paper presents a machine learning model using a random forest (RF) algorithm with the recursive feature elimination (RFE) for the soil liquefaction prediction. The prediction model is tested on 253 CPT-based field data from different earthquakes. RFE, which is one of the feature selection methods, was adopted for eliminating irrelevant features in the mentioned dataset, and then the performance of the RFE-RF (i.e., the model determined by the RFE method) and the RF models with all variables were compared in terms of their performance matrices. The primary focus of this study is to investigate the effectiveness of the feature selection algorithm approach, therefore the raw data that is a benchmark dataset was used to compare the performance of the RFE-RF. The result showed that the RFE approach improved the overall accuracy of the liquefaction prediction.

Keywords:

Liquefaction Prediction, Feature Selection, Machine Learning, Recursive Feature Elimination, Random Forest,

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