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Related Concept Videos

Testing Water Quality01:14

Testing Water Quality

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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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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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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
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Methods of Classification and Identification01:28

Methods of Classification and Identification

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Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
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Related Experiment Video

Updated: Aug 11, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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A water quality assessment method based on an improved grey relational analysis and particle swarm optimization

Rongli Gai1, Zhibin Guo1

  • 1School of Information Engineering, Dalian University, Dalian,  China.

Frontiers in Plant Science
|February 10, 2023
PubMed
Summary

This study introduces an improved grey correlation analysis (ACGRA) and particle swarm optimization multi-classification support vector machine (PSO-MSVM) for accurate river water quality assessment. The novel method effectively evaluates water environments by considering indicator weightings and correlations.

Keywords:
feature selectiongrey relational analysisparticle swarm optimizationsupport vector machinewater quality assessment

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Area of Science:

  • Environmental Science
  • Water Quality Monitoring
  • Data Analysis

Background:

  • Traditional grey correlation analysis (GRA) for river water quality assessment assigns equal weight to indicators, neglecting their varying importance.
  • Localized and 'gray' nature of many water quality indicators necessitates advanced correlation methods for accurate assessment.

Purpose of the Study:

  • To develop an improved river water quality assessment method integrating weighted indicator correlations and advanced machine learning.
  • To enhance the accuracy and reliability of river water environment quality evaluations.

Main Methods:

  • Calculating combined indicator weights using Analytic Hierarchy Process (AHP) and Criteria Importance Though Intercrieria Correlation (CRITIC).
  • Applying improved grey correlation analysis (ACGRA) for feature selection based on weighted indicator correlations.
  • Developing a Particle Swarm Optimization Multi-classification Support Vector Machine (PSO-MSVM) model using ACGRA outputs for water quality assessment.

Main Results:

  • The ACGRA and PSO-MSVM model demonstrated higher accuracy in evaluating river water environment quality across diverse watersheds.
  • Evaluation metrics including accuracy, precision, recall, and root mean square error (RMSE) confirmed the model's effectiveness.
  • The proposed method successfully addressed the limitations of traditional GRA by incorporating indicator weightings.

Conclusions:

  • The integrated ACGRA and PSO-MSVM approach provides a more accurate and robust method for river water quality assessment.
  • This study offers a valuable tool for environmental monitoring and management of river ecosystems.
  • The findings highlight the importance of considering indicator importance and interdependencies in water quality analysis.