Designing an explainable bio-inspired model for suspended sediment load estimation: eXtreme Gradient Boosting coupled with Marine Predators Algorithm

Designing an explainable bio-inspired model for suspended sediment load estimation: eXtreme Gradient Boosting coupled with Marine Predators Algorithm

This study aimed to develop an accurate and reliable model for predicting suspended sediment load (SL) in river systems, which is crucial for water resource management and environmental protection. While Xtreme Gradient Boosting (XGB), a powerful ensemble machine learning (ML) model, has been employ...

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Bibliographic Details
Main Authors: Roozbeh Moazenzadeh, Okan Mert Katipoğlu, Ahmadreza Shateri, Hamid Nasiri, Mohammed Abdallah
Format: Article
Language:English
Published: Taylor & Francis Group 2024-12-01
Series:Engineering Applications of Computational Fluid Mechanics
Subjects:
Suspended sediment load
Machine learning
Hybrid algorithm
Data-driven models
Sediment concentration
SHAP
Online Access:https://www.tandfonline.com/doi/10.1080/19942060.2024.2391449
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