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Related Concept Videos

Quality of Water01:19

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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...
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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...
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A data-driven model for real-time water quality prediction and early warning by an integration method.

Tao Jin1,2, Shaobin Cai3, Dexun Jiang4

  • 1College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China.

Environmental Science and Pollution Research International
|August 24, 2019
PubMed
Summary

A new data-driven model improves surface water quality prediction for real-time early warning. This sustainable water management approach enhances emergency response capabilities using historical data analysis.

Keywords:
Back-propagation neural networkEarly warningImproved genetic algorithmSurface water qualityWater quality prediction

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Data Science

Background:

  • Surface water quality deterioration necessitates advanced prediction techniques.
  • Real-time early warning systems are crucial for effective emergency response and sustainable water management.

Purpose of the Study:

  • To develop an effective data-driven model for surface water quality prediction.
  • To provide real-time early warnings by analyzing historical water quality data.
  • To enhance emergency response capabilities for sustainable water management.

Main Methods:

  • Integration of an improved genetic algorithm (IGA) for optimizing neural network initial weights.
  • Utilization of a back-propagation neural network (BPNN) for adjusting network architectures and identifying water quality variation features.
  • Application of the developed model to forecast surface water quality in the Ashi River, China.

Main Results:

  • The developed data-driven model significantly improved prediction accuracy and reliability compared to a simple BPNN.
  • The model effectively identified inherent water quality variation tendencies.
  • Real-time early warnings were successfully provided for emergency response.

Conclusions:

  • The proposed data-driven model offers a robust solution for surface water quality prediction.
  • The integration of IGA and BPNN enhances model performance for early warning systems.
  • This approach contributes to improved sustainable water management and emergency preparedness.