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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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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...
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Quality of Water

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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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Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
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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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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Watershed Planning within a Quantitative Scenario Analysis Framework
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Data-driven interval credibility constrained quadratic programming model for water quality management under

Qianqian Zhang1, Zhong Li2

  • 1Chengdu University of Information Technology, Chengdu, 610225, China; Department of Civil Engineering, McMaster University, 1280 Main Street West, Hamilton, L8S 4L7, ON, Canada.

Journal of Environmental Management
|June 5, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient data-driven model for water quality management, significantly reducing computational load. It provides robust phosphorus control strategies for watersheds, even with uncertainties.

Keywords:
DICCQPData-driven modelingGrand River simulation modelInexact linear regressionUncertaintyWater quality management

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Area of Science:

  • Environmental Engineering
  • Water Resource Management
  • Computational Modeling

Background:

  • Integrated simulation-optimization modeling offers comprehensive water quality management (WQM) but faces practical implementation challenges.
  • Existing methods often struggle with computational burden and quantifying uncertainties in WQM systems.

Purpose of the Study:

  • To develop an efficient, data-driven simulation-optimization approach for WQM under uncertainty.
  • To enhance the practical applicability of WQM models by reducing computational demands.
  • To incorporate advanced programming techniques for handling nonlinearity and uncertainty.

Main Methods:

  • Integrated a water quality simulation model with an optimization model, substituting it with numerical surrogate models based on inexact linear regression.
  • Employed hybrid inexact programming, specifically interval quadratic programming and credibility constrained programming, for uncertainty quantification and nonlinearity.
  • Applied the data-driven interval credibility constrained quadratic programming (DICCQP) model to the Grand River watershed for phosphorus control.

Main Results:

  • The DICCQP model achieved high computational efficiency, significantly reducing the burden compared to traditional methods.
  • Reliable and robust interval solutions for phosphorus control strategies were obtained under various confidence levels.
  • The model successfully addressed nonlinearity and uncertainties in the water quality management system.

Conclusions:

  • The proposed data-driven approach offers a computationally efficient and robust framework for WQM.
  • The DICCQP model provides practical solutions for controlling phosphorus concentrations, adaptable to other watersheds.
  • This framework supports complex, large-scale water quality management and planning problems effectively.