Predicting Bulk Average Velocity with Rigid Vegetation in Open Channels Using Tree-Based Machine Learning: A Novel Approach Using Explainable Artificial Intelligence

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 12; p. 4398
• Main Authors: Meddage, D. P. P., Ekanayake, I. U., Herath, Sumudu, Gobirahavan, R., Muttil, Nitin, Rathnayake, Upaka
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 10.06.2022; MDPI
• Subjects: Artificial intelligence; bulk average velocity; explainable artificial intelligence; Floods; Friction factor; Hydrology; Methods; Model accuracy; Neural networks; Open channels; rigid vegetation; Rivers; Stream flow; Support vector machines; Surface layers; tree-based machine learning; Vegetation; Velocity
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22124398

Predicting the bulk-average velocity (UB) in open channels with rigid vegetation is complicated due to the non-linear nature of the parameters. Despite their higher accuracy, existing regression models fail to highlight the feature importance or causality of the respective predictions. Therefore, we propose a method to predict UB and the friction factor in the surface layer (fS) using tree-based machine learning (ML) models (decision tree, extra tree, and XGBoost). Further, Shapley Additive exPlanation (SHAP) was used to interpret the ML predictions. The comparison emphasized that the XGBoost model is superior in predicting UB (R = 0.984) and fS (R = 0.92) relative to the existing regression models. SHAP revealed the underlying reasoning behind predictions, the dependence of predictions, and feature importance. Interestingly, SHAP adheres to what is generally observed in complex flow behavior, thus, improving trust in predictions.