An intelligent traceability method of water pollution based on dynamic multi-mode optimization

Area of Science:

• Environmental Science
• Water Resource Management
• Computational Science

Background:

• Drinking water safety is a critical societal concern.
• Sudden water pollution events necessitate rapid source identification for effective emergency response.
• Existing methods struggle with the non-unique and dynamic nature of pollution sources.

Purpose of the Study:

• To develop an intelligent algorithm for real-time water pollution source traceability.
• To address the challenges of non-uniqueness and dynamic changes in pollution sources.
• To provide technical support for emergency management in pollution incidents.

Main Methods:

• Designed an intelligent traceability algorithm based on dynamic multi-mode optimization.
• Implemented an optimal subpopulation division strategy for improved local optimization.
• Utilized a similar peak penalty strategy to reduce solution non-uniqueness.
• Incorporated historical information preservation and adaptive initialization for dynamic problem adaptation.

Main Results:

• The proposed algorithm accurately traces pollution sources in real-time.
• It effectively obtains characteristic information of pollution sources.
• Demonstrated improved convergence rates and effectiveness in dynamic scenarios.
• Significantly reduced the number of non-unique solutions.

Conclusions:

• The developed algorithm is effective for real-time water pollution source tracing.
• It successfully overcomes the limitations of non-unique and dynamic pollution sources.
• Provides a valuable tool for enhancing emergency management decision-making in water pollution events.