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Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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A bi-objective mixed-integer linear programming model to optimize thinning schedules in wildfire-prone Pinus

Rafael M Navarro-Cerrillo1, Mauricio Acuna2, Antonio Jesús Ariza-Salamanca1

  • 1Department of Forestry, School of Agriculture and Forestry, University of Córdoba, DendrodatLab-ERSAF; Laboratory of Dendrochronology, Silviculture and Climate Change, Edif. Leonardo da Vinci, Campus de Rabanales s/n, 14071 Córdoba, Spain.

The Science of the Total Environment
|May 1, 2025
PubMed
Summary

Implementing forest thinning schedules in Canary Islands pine forests can significantly reduce fire risk and improve environmental outcomes. This approach balances economic returns with fire suppression difficulty, offering optimal solutions for forest management.

Keywords:
Multi-objective linear programmingOptimizationPareto frontier timber managementPine plantationsSuppression difficulty indexThinning scheduling

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Area of Science:

  • Forestry Science
  • Environmental Management
  • Operations Research

Background:

  • Pinus canariensis forests face frequent fire risks, impacting wood supply and necessitating mitigation strategies.
  • Forest managers use multi-objective programming to balance conflicting goals like economic return and fire suppression.
  • Silvicultural strategies, such as thinning, are explored to reduce fire's adverse effects.

Purpose of the Study:

  • To develop an integrated modelling framework for computing Pareto frontiers in forest management.
  • To maximize economic returns while minimizing fire suppression difficulty in Pinus canariensis forests.
  • To evaluate the impact of varying thinning schedules on fire risk and economic outcomes across a geographic gradient.

Main Methods:

  • Utilized a bi-objective mixed-integer linear programming (ɛ-constrained) approach.
  • Developed a modelling framework to compute Pareto frontiers by integrating economic returns and fire suppression difficulty.
  • Assessed fire extinction risk and evaluated thinning scenarios across Tenerife, Canary Islands.

Main Results:

  • Reduced fire suppression difficulty (SDI) showed limited impact on economic return (under 1.275).
  • Thinning schedules and the relative tolerance parameter (β) significantly influenced total SDI and the number of stands thinned.
  • Variations in β affected average SDI, thinned area, wood flows, thinning costs, and total return.

Conclusions:

  • Effective thinning schedules in fire-prone forests enhance environmental outcomes, even with minor reductions in net economic benefits.
  • The Pareto frontier provides multiple optimal solutions, requiring decision-makers to select the most operationally feasible option.
  • Integrated modelling frameworks are crucial for balancing competing objectives in sustainable forest management.