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

Abstract

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.