An ensemble data-driven approach for incorporating uncertainty in the forecasting of stormwater sewer surcharge

Abstract

ABSTRACT Stormwater surcharge events are unavoidable above a certain rainfall intensity. Thus, for protection and damage mitigation, forecast systems are of outstanding importance. This study develops an ensemble forecast system (EFS) to predict the beginning and end of sewer surcharge events. It applies a nonlinear autoregressive with exogenous inputs (NARX) network to each member of the ensemble, making it suitable for real-time predictions. The fundamental idea is the forecast of water depth time series within manholes based on the given rainfall. The novelty lies in the consideration of uncertainty through the ensemble structure for which the numbers of neurons in the hidden layer, the weights, and biases are considered to be uncertain. The results are evaluated based on observed values captured within the uncertainty band ), and the width of the band ). The varied between 74% and 94% and the between 1.36 and 10.68.