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Related Concept Videos

Testing Water Quality01:14

Testing Water Quality

203
When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...
203
Quality of Water01:19

Quality of Water

223
In concrete preparation, the quality of water is paramount as it affects the strength and durability of the concrete. Potable water is usually preferred; however, it must not have excessive sodium or potassium to prevent compromising the concrete's integrity. Water quality is typically evaluated based on impurities such as dissolved solids, chlorides, and sulfates, and its pH value is ideally between 6 and 8. Even slightly acidic natural water may be acceptable unless it contains harmful...
223
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

131
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
131

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Related Experiment Video

Updated: Oct 2, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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

Qinghua Wu1, Bin Wu2, Xuesong Yan3

  • 1Hubei Provincial Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, 430205 China.

Neural Computing & Applications
|February 28, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an intelligent algorithm for real-time water pollution source tracing. The novel method effectively addresses challenges like non-unique and dynamic pollution sources, ensuring accurate identification and characteristic information retrieval.

Keywords:
DynamicInitialization strategiesMulti-mode optimizationPollution intelligent traceabilitySimulation optimization

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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.