Data assimilation in hydrodynamic models for system-wide soft sensing and sensor validation for urban drainage tunnels

Abstract

Tunnels are increasingly used worldwide to expand the capacity of urban drainage systems, but they are difficult to monitor with sensors alone. This study enables soft sensing of urban drainage tunnels by assimilating water level observations into an ensemble of hydrodynamic models. Ensemble-based data assimilation is suitable for non-linear models and provides useful uncertainty estimates. To limit the computational cost, our proposed scheme restricts the assimilation and ensemble implementation to the tunnel and represents the surrounding drainage system deterministically. We applied the scheme to a combined sewer overflow tunnel in Copenhagen, Denmark, with two sensors 3.4 km apart. The downstream observations were assimilated, while those upstream were used for validation. The scheme was tuned using a high-intensity event and validated with a low-intensity one. In a third event, the scheme was able to provide soft sensing as well as identify errors in the upstream sensor with high confidence. HIGHLIGHTS We propose a data assimilation scheme tailor-made for urban drainage tunnels that can efficiently assimilate observations into an ensemble of 1D hydrodynamic models.; We tested and validated our methodology with a real case study.; The results support our hypothesis that the scheme is capable of promoting the hydrodynamic model to a soft sensing tool, which can be further used for validating physical sensors.;

Keywords

• combined overflow tunnel
• data assimilation
• distributed urban drainage models
• model-based sensor validation
• soft sensing
• urban drainage tunnel