Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Probability Laws01:49

Probability Laws

44.4K
Overview
44.4K
Regression Toward the Mean01:52

Regression Toward the Mean

7.1K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
7.1K
Probability Distributions01:32

Probability Distributions

12.2K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
12.2K
Multiple Regression01:25

Multiple Regression

4.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
4.0K
Design Example: Automobile Ignition System01:14

Design Example: Automobile Ignition System

554
The automobile's ignition system plays a vital role by ensuring the timely ignition of the fuel-air mixture in each cylinder. This ignition is facilitated by a spark plug, which is composed of two electrodes separated by an air gap. A spark forms across this air gap when a substantial voltage is generated between the electrodes, leading to the ignition of the fuel.
One can generate a large voltage using a car battery of 12 volts with the help of inductors. Inductors are known for opposing...
554
Probability in Statistics01:14

Probability in Statistics

23.5K
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
23.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Biobjective Stochastic Model for Intermodal Supply Chains: Application to the Corn and Soybean Flows.

Industrial & engineering chemistry research·2026
See all related articles

Related Experiment Video

Updated: Feb 7, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.4K

Estimating wildfire ignition probabilities with geographic weighted logistic regression.

Marco Marto1, Sarah Santos1, António Vieira2

  • 1ALGORITMI Research Center/LASI, University of Minho, Braga, Portugal.

Journal of Applied Statistics
|February 6, 2026
PubMed
Summary

This study models wildfire ignition probabilities in Baião, Portugal, using geographic weighted regression. Findings help authorities identify high-risk areas for improved wildfire management and resource allocation.

Keywords:
Fire ignitionsGWLRfire probabilitylogistic regressionmachine learningspatial regression

More Related Videos

How to Ignite an Atmospheric Pressure Microwave Plasma Torch without Any Additional Igniters
08:42

How to Ignite an Atmospheric Pressure Microwave Plasma Torch without Any Additional Igniters

Published on: April 16, 2015

20.7K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.9K

Related Experiment Videos

Last Updated: Feb 7, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.4K
How to Ignite an Atmospheric Pressure Microwave Plasma Torch without Any Additional Igniters
08:42

How to Ignite an Atmospheric Pressure Microwave Plasma Torch without Any Additional Igniters

Published on: April 16, 2015

20.7K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.9K

Area of Science:

  • Environmental Science
  • Geospatial Analysis
  • Risk Management

Background:

  • Wildfire ignition probabilities are crucial for effective wildfire management, risk assessment, and resource prepositioning.
  • Northern Portugal, specifically the Baião municipality, experiences frequent wildfires during its fire season.
  • Accurate ignition probability data can significantly aid firefighting authorities in identifying vulnerable areas and combating fire occurrences.

Purpose of the Study:

  • To estimate fire ignition probabilities in the Baião municipality, Portugal.
  • To develop quantitative models for wildfire risk management and resource allocation.
  • To assist local authorities in identifying fire-prone zones for enhanced wildfire response.

Main Methods:

  • Geographically Weighted Regression (GWLR) with an exponential kernel was employed to estimate ignition probabilities.
  • Logit and probit link functions were utilized alongside independent variables: population density, distance to roads, altitude, forest proportion (land use), and Normalized Difference Moisture Index (NDMI) from LANDSAT 8.
  • A binary dependent variable (wildfire ignition occurrence 2011-2020) was used, with data split into training (70%) and test sets via stratified sampling.

Main Results:

  • The study generated useful application models for estimating wildfire ignition probabilities.
  • Model performance was rigorously evaluated using metrics such as accuracy, ROC curve AUC, precision, recall, specificity, balanced accuracy, and F1 score.
  • The developed models offer valuable insights for wildfire management in Portugal, complementing existing reference models.

Conclusions:

  • The developed GWLR models provide a robust framework for predicting wildfire ignition probabilities.
  • These models can be integrated into quantitative risk management strategies for fuel and resource management.
  • The findings support enhanced decision-making for firefighting authorities in fire-prone regions like Baião.