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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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The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
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The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Relative Risk01:12

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Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
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Updated: Jul 19, 2025

An R-Based Landscape Validation of a Competing Risk Model
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Machine learning-enabled regional multi-hazards risk assessment considering social vulnerability.

Tianjie Zhang1, Donglei Wang2, Yang Lu3

  • 1Environmental Research Building, Department of Computer Science, Boise State University, Boise, ID, 83725, USA.

Scientific Reports
|August 17, 2023
PubMed
Summary

This study introduces a machine learning approach to assess multi-hazards risk, integrating social vulnerability for better preparedness. The findings highlight high-risk areas needing urgent policy intervention for natural hazard mitigation.

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Area of Science:

  • Environmental Science
  • Geospatial Analysis
  • Risk Management

Background:

  • Regional multi-hazards risk assessment is complex due to data access and understanding interactions between hazards and social vulnerability.
  • Effective natural hazard risk perception and preparedness require studying hazard distribution, especially in high-risk and socially vulnerable areas.

Purpose of the Study:

  • To propose and validate a novel multi-hazards risk assessment method incorporating social vulnerability using machine learning.
  • To develop a spatial understanding of multi-hazards and social vulnerability interactions for targeted risk reduction strategies.

Main Methods:

  • Characterization and mapping of multi-hazards (flooding, wildfires, seismic) using five machine learning models (NB, KNN, LR, RF, KM).
  • Evaluation of social vulnerability using a composite index and machine learning classification.
  • Quantification of spatial interactions between multi-hazards and social vulnerability.

Main Results:

  • The Random Forest (RF) model demonstrated superior performance for both hazard and social vulnerability datasets.
  • Cities facing multi-hazards risk constitute 34.12% of the study area, covering 20.80% of the land.
  • High multi-hazards risk and socially vulnerable cities represent 15.88% of the total area, covering 4.92% of the land.

Conclusions:

  • The study generated a multi-hazards risk map revealing diverse spatial patterns of high-hazard and vulnerable regions.
  • Emphasizes the critical need for information-based prioritization and effective policy measures to mitigate natural hazard risks.