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Machine learning-based surrogate model assisting stochastic model predictive control of urban drainage systems.

Xinran Luo1, Pan Liu1, Qian Xia2

  • 1State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, 430072, China; Hubei Provincial Key Lab of Water System Science for Sponge City Construction, Wuhan University, Wuhan, 430072, China; Research Institute for Water Security (RIWS), Wuhan University, Wuhan, 430072, China.

Journal of Environmental Management
|September 15, 2023
PubMed
Summary

This study introduces a machine learning surrogate model to quickly assess urban drainage system performance. This enables a new real-time control strategy that significantly improves system resilience to stormwater inflow uncertainty.

Keywords:
Combined sewer overflowMachine learning-based surrogate modelResilienceStochastic model predictive controlUrban drainage system

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Area of Science:

  • Environmental Engineering
  • Hydrology
  • Artificial Intelligence

Background:

  • Urban drainage systems (UDSs) require robust real-time control to manage stormwater inflow uncertainty and enhance resilience.
  • Current uncertainty-addressing methods for UDSs are computationally intensive, limiting their practical application.
  • Accurate quantification of stormwater inflow uncertainty is crucial for effective UDS operation.

Purpose of the Study:

  • To develop a computationally efficient machine learning-based surrogate model (MLSM) for UDSs.
  • To integrate MLSM with stochastic model predictive control for a novel real-time control strategy.
  • To minimize expected system overflow by considering stochastic stormwater inflow scenarios.

Main Methods:

  • Developed a machine learning-based surrogate model (MLSM) to emulate high-fidelity urban drainage dynamics.
  • Combined MLSM with stochastic model predictive control for real-time operational strategy.
  • Generated and reduced an ensemble of stormwater inflow scenarios using statistical methods.

Main Results:

  • MLSM achieved a 99.1% reduction in computational time compared to the original model while maintaining high fidelity.
  • The proposed MLSM-based stochastic control strategy significantly outperformed deterministic control in enhancing system resilience.
  • The strategy demonstrated effectiveness for real-time operation in a complex UDS in China.

Conclusions:

  • The MLSM significantly reduces computational burden, making advanced control strategies feasible for real-time UDS management.
  • The developed real-time control strategy effectively enhances UDS resilience against stormwater inflow uncertainty.
  • This approach offers a promising solution for improving the operational efficiency and reliability of complex urban drainage systems.