Hyperparameter tuning of supervised bagging ensemble machine learning model using Bayesian optimization for estimating stormwater quality

Physically based models (PBMs), including stormwater management model (SWMM), require a significant amount of in situ data and expertise to predict water quality in urban watersheds. In recent years, data-driven models have been increasingly used as an alternative for the prediction of pollutant concentrations. Supervised machine learning (ML) models have been used for estimating stormwater quality parameters. However, optimizing the structure of such ML models has rarely been considered. This study aims to comprehensively evaluate the optimization of the supervised ensemble bagging ML model for forecasting stormwater quality using an ML-based optimization method called Bayesian optimization (BO). To that end, a bagging ensemble model, namely random forest (RF), was first developed for estimating total suspended solids (TSS) concentration in urban watersheds. Eleven factors, including drainage area, land-use types, impervious area, rainfall depth, the volume of runoff, and antecedent dry days, were implemented as predictive features in the model, and their data were acquired from the National Stormwater Quality Database (NSQD). Values for the number of basic estimators, the number of basic selected features for developing basic estimators, subsamples, and the maximum depth of basic learners were optimized using BO. A sensitivity analysis was done on the ML model and the BO parameters, including acquisition function, number of initial points, and realizations. Results indicated that the accuracy of the RF model depends on all mentioned RF parameters. The performance of the best-developed RF model was satisfactory in both the training and the testing steps. This model obtained the R2 values of 0.955 and 0.915 for the training and testing step, respectively. The study demonstrated the potential of a combination of the RF models and BO for accurately predicting stormwater quality parameters.