Newswise – The complexity of integrating wastewater systems and urban flows into a comprehensive model has long been a challenge due to high computational demands and limited monitoring data. Traditional calibration methods cannot effectively address these challenges.

A recent one study (https://doi.org/10.1016/j.ese.2023.100320) Published in Environmental science and ecotechnology (Volume 18, 2024) introduces an advanced machine learning system designed to improve the accuracy and efficiency of wastewater system modeling. This innovative technique significantly reduces the time required for parameter calibration and increases the precision of urban water pollution predictions.

At the heart of this groundbreaking research is the ingenious combination of two advanced technologies: Ant Colony Optimization (ACO) and Long Short-Term Memory (LSTM) networks, integrated into a Machine Learning Parallel System (MLPS). ACO is inspired by the foraging behavior of ants to find the most efficient paths and is applied here to navigate the complex parameter space of water models. Meanwhile, LSTM networks, a type of recurrent neural network, excel at recognizing patterns in sequential data, making them ideal for understanding the temporal dynamics of pollutants in wastewater flow systems. By combining these technologies, researchers have developed an MLPS capable of performing rapid and precise calibrations of sewer flow models. Traditional methods, which are often cumbersome and time-consuming, cannot match the efficiency or accuracy of this new approach. In particular, the MLPS dramatically reduces calibration time from potentially months to just a few days, without compromising the model’s ability to accurately predict pollution levels.

Highlights

  • A model calibration method is created using model replacement and algorithm optimization.
  • The process-based models and machine learning interact in unique ways.
  • The optimization time of the integrated process-based model was saved by 89.94%.
  • The accuracy of complex models can be improved based on limited data.

Dr. Yu Tian, ​​lead author of the study, explains: “The integration of ant colony optimization and long-short-term memory algorithms into our parallel machine learning system represents a significant advance in environmental management. It enables rapid and accurate model calibration with limited data , which open up new avenues for urban water system planning and pollution control.”

MLPS provides a robust solution for accurately simulating urban water quality, essential for effective environmental management. Its ability to quickly adapt to new data and scenarios makes it a valuable tool for urban planners and environmental scientists, facilitating the development of targeted pollution control strategies and sustainable water management practices.

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References

DOI

10.1016/j.ese.2023.100320

Original source URL

https://doi.org/10.1016/j.ese.2023.100320

Financing information

This study was supported by the National Key R&D Program of China (2019YFD1100300) and the Fellowship of China Postdoctoral Science Foundation (2020M681105). The authors also thank the State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology (No. 2021TS23).

Around Environmental science and ecotechnology

Environmental science and ecotechnology (ISSN 2666-4984) is an international, peer-reviewed and open access journal published by Elsevier. The journal publishes key views and research across the spectrum of ecology and environmental sciences, such as climate change, sustainability, biodiversity conservation, environment and health, green catalysis/processing for pollution control, and AI-driven environmental engineering. According to the Journal Citation Report, ESE’s current impact factor is 12.6TM 2022.

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