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

Response Surface Methodology01:16

Response Surface Methodology

209
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
209
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

92
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
92
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

74
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
74
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

164
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:
164
Multiple Regression01:25

Multiple Regression

3.1K
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...
3.1K
Regression Analysis01:11

Regression Analysis

5.9K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.9K

You might also read

Related Articles

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

Sort by
Same author

Lung cancer risk in relation to indicative radon atlas metrics in Northern Ireland: a population-based case-control study using secondary data.

Environmental geochemistry and health·2026
Same author

Radon risk mapping in Spain: a population and building-inclusive approach.

Environment international·2026
Same author

Humidity-dependent radon retention in zeolite and non-zeolite sorbents for <sup>225</sup>Ac production facilities.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2026
Same author

Frequency of driver oncogenic alterations in NSCLC and estimated indoor radon exposure in Europe (RADON EUROPE study).

Therapeutic advances in medical oncology·2025
Same author

Radon-thoron exhalation and emanation determinations from mylonitic rock samples collected in north Abu Rusheid, Egypt.

Environmental monitoring and assessment·2025
Same author

From collective to individual radon risk exposure: An insight into the current European regulation.

Environment international·2025

Related Experiment Video

Updated: Aug 12, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

245

Machine learning in environmental radon science.

Javier Elío1, Eric Petermann2, Peter Bossew3

  • 1Department of Mechanical and Marine Engineering, Western Norway University of Applied Sciences, Inndalsveien 28, Bergen, 5063, Norway.

Applied Radiation and Isotopes : Including Data, Instrumentation and Methods for Use in Agriculture, Industry and Medicine
|January 27, 2023
PubMed
Summary

Machine learning (ML) models analyze environmental radon dynamics using factors like meteorology and geology. These tools predict indoor radon levels, aiding in mapping and time-series analysis for better understanding.

Keywords:
Environmental radonEstimationMachine learningPrediction

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.5K

Related Experiment Videos

Last Updated: Aug 12, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

245
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.5K

Area of Science:

  • Environmental Science
  • Geoscience
  • Computer Science

Background:

  • Environmental radon concentration exhibits complex spatial and temporal variability.
  • Factors influencing radon include meteorology, geology, soil, hydrogeology, tectonics, seismicity, and anthropogenic elements.
  • Machine learning (ML) offers advanced tools for analyzing and predicting these dynamics.

Purpose of the Study:

  • To provide an overview of ML methods for radon analysis.
  • To discuss the application, validation, and limitations of ML in environmental radon studies.
  • To demonstrate ML applications in geogenic radon mapping and indoor radon time-series analysis.

Main Methods:

  • Overview of various ML algorithms for spatial and time-series analysis.
  • Model development using sample and predictor data for information extraction and prediction.
  • Validation techniques for ML model performance assessment.

Main Results:

  • Successful geogenic radon mapping in Germany using multiple predictors.
  • Time-series analysis of indoor radon concentrations in Chiba, Japan, incorporating meteorological data.
  • Identification of key limitations in current ML techniques for radon prediction.

Conclusions:

  • ML provides powerful tools for understanding and predicting environmental radon dynamics.
  • Further research is needed to overcome the identified limitations of ML techniques.
  • ML applications show promise for improved radon hazard assessment and mitigation strategies.