Using artificial neural networks to determine sediment concentration at limit of deposition in storm sewers

Abstract

The concept of sediment transport at the limit of deposition in storm sewers represents one operational condition that avoid deposition of sediments maintaining the discharge capacity of the pipes. In this study, this condition was analyzed applying one Artificial Neural Network Multilayer Perceptron (ANN-MLP) model to predict the volumetric concentration at the limit of deposition, using 544 experimental data from literature. It was evaluated different input variables combinations and model configurations, showing the sensitivity of the model with these changes. Through this study, it was demonstrated that the proposed model outperforms the existing equations, leading to more assertive predictions in the determination of volumetric concentrations at the limit of deposition, resulting in values of R2 = 0.92, Mean Absolute Percentage Error (MAPE) = 35.09 % and Mean Average Error (MAE) = 59.84 ppm. With the performed analysis, the study selects one equation to be used for extrapolations when determining the volumetric concentration at the limit of deposition in storm sewers. The selected equation is superior due to its theoretical basis. This work includes one more concept to a better methodology in obtaining the conditions of the flow at the limit of deposition.