Sewer System AI Cuts Calibration Time, Boosts Water Quality Forecasting
• Researchers developed a machine learning system to improve sewer-river system models, reducing calibration time and increasing prediction accuracy.
• The system combines Ant Colony Optimization (ACO) and Long Short-Term Memory (LSTM) networks into a machine learning parallel system (MLPS).
• ACO navigates complex parameters spaces efficiently like ants find efficient paths while LSTM recognizes temporal pollutant patterns.
• The MLPS can calibrate models in days instead of months without losing prediction accuracy.
• The system allows faster, more accurate urban water quality simulation to facilitate pollution control and sustainable water management.