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Related Concept Videos

Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Uncertainty: Overview00:59

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...

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Related Experiment Videos

Robust stochastic fuzzy possibilistic programming for environmental decision making under uncertainty.

Xiaodong Zhang1, Guo H Huang, Xianghui Nie

  • 1Environmental Systems Engineering Program, Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan, Canada S4S 0A2.

The Science of the Total Environment
|October 30, 2009
PubMed
Summary

This study introduces a robust chance-constrained fuzzy possibilistic programming (RCFPP) model to manage nonpoint source (NPS) water pollution in agriculture. The RCFPP model provides feasible decision schemes for farming activities under uncertainty, balancing economic goals with water quality protection.

Related Experiment Videos

Area of Science:

  • Environmental Science
  • Agricultural Science
  • Operations Research

Background:

  • Nonpoint source (NPS) water pollution poses significant environmental challenges, particularly within agricultural systems.
  • Existing water quality management models struggle to adequately address the inherent uncertainties in agricultural practices and environmental factors.

Purpose of the Study:

  • To propose a novel robust chance-constrained fuzzy possibilistic programming (RCFPP) model for effective water quality management in agricultural systems.
  • To enhance existing fuzzy possibilistic programming, fuzzy robust programming, and chance-constrained programming approaches to better reflect complex systems under uncertainty.
  • To investigate the trade-offs between regional economic development objectives and water quality/quantity restrictions.

Main Methods:

  • Developed a robust chance-constrained fuzzy possibilistic programming (RCFPP) model by enlarging the uncertain decision space of fuzzy constraints.
  • Improved robustness of optimization processes and solutions by enhancing existing programming approaches.
  • Applied the RCFPP model to a case study involving agricultural system management under various scenarios of necessity and violation probability levels.

Main Results:

  • The RCFPP model successfully generated feasible decision schemes for farming area, manure/fertilizer application, and livestock size under different scenarios.
  • Analysis revealed that pursuing higher agricultural income can decrease the certainty of achieving objectives and potentially violate water standards.
  • Decision variables derived from combined p-necessity and p(i) levels offered valuable insights for refining agricultural management strategies.

Conclusions:

  • The RCFPP model effectively balances agricultural economic goals with water quality management under uncertainty.
  • The developed approach is applicable to practical problems involving simultaneous fuzzy and probabilistic data.
  • Decision-makers can utilize the model's outputs to justify and adjust agricultural management schemes by incorporating implicit knowledge.