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Related Concept Videos

Testing Water Quality01:14

Testing Water Quality

172
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...
172
Quality of Water01:19

Quality of Water

175
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...
175

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Water Quality Prediction Based on SSA-MIC-SMBO-ESN.

Yan Kang1, Jinling Song1, Zhuo Lin1

  • 1School of Mathematics and Information Science & Technology, Hebei Normal University of Science & Technology, Key Laboratory of Ocean Dynamics and Resources and Environments, Hebei Agricultural Data Intelligent Perception and Application Technology Innovation Center, Qinhuangdao 066000, Hebei, China.

Computational Intelligence and Neuroscience
|August 15, 2022
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Summary
This summary is machine-generated.

Accurate water quality prediction is vital for managing pollution. This study introduces advanced Echo State Network (ESN) models, enhanced with singular spectrum analysis and maximum information coefficient, to precisely forecast dissolved oxygen, permanganate index, and total phosphorus levels.

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Area of Science:

  • Environmental Science
  • Data Science
  • Water Resource Management

Background:

  • Water pollution poses significant risks to human activities and ecosystems.
  • Proactive water quality prediction is crucial for effective water resource management.
  • Existing prediction methods may be limited by data noise and inter-variable correlations.

Purpose of the Study:

  • To develop accurate predictive models for key water quality indicators: dissolved oxygen (DO), permanganate index (CODMn), and total phosphorus (TP).
  • To leverage the temporality of water quality data using Echo State Networks (ESN).
  • To enhance prediction accuracy by incorporating data denoising and feature correlation analysis.

Main Methods:

  • Data preprocessing included imputation of missing values and outlier correction using Z-score and linear trend methods.
  • Singular Spectrum Analysis (SSA) was employed for denoising time-series water quality data.
  • Maximum Information Coefficient (MIC) was used to identify strong correlations between water quality indices for feature selection.
  • Multi-feature water quality prediction models were built using offline and online learning algorithms of ESN.
  • Hyperparameter optimization was performed using Sequential Model-Based Optimization (SMBO).

Main Results:

  • The developed SSA-MIC-SMBO-Offline ESN and SSA-MIC-SMBO-Online ESN models demonstrated high accuracy in predicting DO, CODMn, and TP.
  • Data denoising via SSA effectively improved model performance by mitigating noise interference.
  • Feature selection based on MIC identified relevant indices, enhancing the predictive power of the ESN models.

Conclusions:

  • The proposed ESN-based models offer a robust and accurate approach for water quality prediction.
  • These models provide valuable tools for water management authorities to anticipate and respond to pollution events.
  • The integration of SSA, MIC, and SMBO with ESN represents an effective strategy for time-series water quality forecasting.