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Related Experiment Video

Updated: Jan 7, 2026

Parameterizing V-notch Weir Equations for Flow Monitoring in a Drainage Control Structure
07:15

Parameterizing V-notch Weir Equations for Flow Monitoring in a Drainage Control Structure

Published on: April 25, 2025

915

Differentiable neural network-based models enable gradient-based optimization for model predictive control of urban

Zhiyu Zhang1, Wenchong Tian2, Zhenliang Liao3

  • 1School of Energy and Environment, City University of Hong Kong, Hong Kong SAR, China; College of Environmental Science and Engineering, Tongji University, 200092, Shanghai, China; State Key Laboratory of Marine Environmental Health, City University of Hong Kong, Hong Kong SAR, China; City University of Hong Kong Shenzhen Research Institute, Shenzhen, China.

Water Research
|December 25, 2025
PubMed
Summary

Model predictive control (MPC) for urban drainage networks significantly reduces flooding. This new framework uses neural networks for faster, real-time optimization, outperforming traditional methods.

Keywords:
Gradient-based optimizationModel predictive controlNeural networkReal-time controlUrban drainage network

Related Experiment Videos

Last Updated: Jan 7, 2026

Parameterizing V-notch Weir Equations for Flow Monitoring in a Drainage Control Structure
07:15

Parameterizing V-notch Weir Equations for Flow Monitoring in a Drainage Control Structure

Published on: April 25, 2025

915

Area of Science:

  • Environmental Engineering
  • Water Resources Management
  • Artificial Intelligence in Civil Engineering

Background:

  • Urban drainage networks face challenges with overflow and flooding due to limited hydraulic capacity.
  • Model predictive control (MPC) offers optimization potential but is computationally intensive.
  • Current MPC methods rely on physics-based models, hindering real-time application.

Purpose of the Study:

  • To develop an efficient Model Predictive Control (MPC) framework for urban drainage networks.
  • To accelerate the online optimization process for hydraulic capacity management.
  • To enable real-time MPC application in catchment-scale drainage systems.

Main Methods:

  • Implemented a novel MPC framework utilizing a gradient-based optimizer with a second-order Quasi-Newton method.
  • Employed differentiable neural network-based internal models (black-box and spatio-temporal) for efficient system evaluation.
  • Compared performance against a benchmark genetic algorithm using both neural network and physics-based models.

Main Results:

  • The proposed gradient-based MPC framework achieved optimization speeds 100-1000 times faster than the benchmark genetic algorithm.
  • Comparable or superior control performance in overflow mitigation was observed, particularly with the spatio-temporal neural network model.
  • Demonstrated the feasibility of real-time MPC implementation for large-scale urban drainage networks.

Conclusions:

  • The developed MPC framework significantly enhances computational efficiency for urban drainage network optimization.
  • Differentiable neural networks integrated with gradient-based optimization enable real-time control capabilities.
  • This approach offers a practical solution for mitigating urban flooding and improving hydraulic capacity utilization.