Abstract:
Sediment transportation in water bodies may cause many problems for the water resources projects and damage
the environment. Hence, modeling sediment load components, including suspended sediment load (SSL) and
bedload (BL) in rivers is of prime importance. Effective modeling of SSL and BL remains a challenging task due to
their complex hydrological process. On this account, this study aims to appraise the potential of conventional
machine learning (ML) models including adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and their integrative version with nature optimization algorithm called genetic algorithm (GAANFIS and GA-SVR) for SSL and BL prediction. Two traditional models are developed for modeling verification
including the sediment rating curve (SRC) and multiple linear regression (MLR). The modeling results are assessed using four statistical measures (e.g., root mean square error (RMSE), mean absolute error (MAE),
Nash–Sutcliffe Efficiency (NSE), and coefficient of determination (R2
)), diagnostic analysis (scatter plots and
Taylor diagram), and evaluation of the dependence of the state of the river flow-sediment system (hysteresis
analysis). Based on the attained predictability performance, the integrative ML models reveal a superior prediction capacity in comparison with the standalone ANFIS, SVR, and the traditional models. In quantitative
evaluation, the proposed integrative ML models indicate a remarkable prediction enhancement approximately
44% mean magnitude based on the MAE metric against the SRC traditional model for both the SSL and BL
predicted values. Overall, the current investigation evidences the potential of the nature-inspired algorithm as a
hyper-parameter optimizer for ML models that produce a reliable and robust predictive model for sediment
concentration quantification
