Foul sewer model development using geotagged information and smart water meter data

Area of Science:

• Environmental Engineering
• Hydraulic Engineering
• Urban Water Management

Background:

• Foul sewer system (FSS) hydraulic modeling is crucial for effective management.
• Traditional FSS model development is hindered by insufficient field measurement data due to limited in-situ sensors.

Purpose of the Study:

• To propose a novel method for developing FSS models using accessible geotagged and smart water meter data.
• To improve the accuracy of FSS hydraulic simulations and uncertainty analysis.

Main Methods:

• Associating sewer manholes with population estimates derived from geotagged data.
• Implementing a two-stage optimization framework to determine daily time-series inflows.
• Developing an uncertainty analysis method by linking smart meter water consumption data to wastewater discharge variations.

Main Results:

• The proposed method significantly outperforms traditional approaches in simulating FSS hydraulic variables.
• Accurate simulation of manhole water depths and sewer flows, including their uncertainty ranges.
• Demonstrated effectiveness using two real-world FSS case studies.

Conclusions:

• The novel method offers a promising solution for FSS model development.
• Leverages easily obtainable geotagged and smart water meter data.
• Enhances understanding and management of foul sewer systems through improved modeling.