Machine learning-based prediction of unconfined compressive strength and contaminant leachability in dredged contaminated sediments for land reclamation projects.

This research investigates the application of machine learning techniques for predicting unconfined compressive strength (UCS) and contaminant leachability in dredged contaminated sediments (DCS) with implications for land reclamation projects. Traditionally, determining these parameters has been challenging, costly, and time-consuming, hindering efficient project planning and execution. Therefore, this study evaluated the efficacy of two machine learning models, namely extreme gradient boosting (XGBoost) and decision tree (DT), in improving prediction accuracy and reducing the need for resource-intensive testing procedures.