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

Cluster Sampling Method01:20

Cluster Sampling Method

11.6K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.6K
Stratified Sampling Method01:16

Stratified Sampling Method

11.7K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
11.7K
Sampling Plans01:23

Sampling Plans

165
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.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
165
Sampling Methods: Overview01:06

Sampling Methods: Overview

269
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
269
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

177
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
177
Survival Tree01:19

Survival Tree

52
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.
 Building a Survival Tree
Constructing a...
52

You might also read

Related Articles

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

Sort by
Same author

Personalized Smart Home Automation Using Machine Learning: Predicting User Activities.

Sensors (Basel, Switzerland)·2025
Same author

Towards Developing a Robust Intrusion Detection Model Using Hadoop-Spark and Data Augmentation for IoT Networks.

Sensors (Basel, Switzerland)·2022
Same author

Activity Recognition in Residential Spaces with Internet of Things Devices and Thermal Imaging.

Sensors (Basel, Switzerland)·2021
See all related articles

Related Experiment Video

Updated: May 28, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K

Spatio-Temporal Agnostic Sampling for Imbalanced Multivariate Seasonal Time Series Data: A Study on Forest Fires.

Abdul Mutakabbir1, Chung-Horng Lung2, Kshirasagar Naik3

  • 1Department of Data Science, Analytics, and Artificial Intelligence, Carleton University, Ottawa, ON K1S 5B6, Canada.

Sensors (Basel, Switzerland)
|February 13, 2025
PubMed
Summary

This study introduces Spatio-Temporal Agnostic Sampling (STAS) to address imbalanced data for forest fire prediction. STAS effectively improves both fire probability classification and severity assessment models.

Keywords:
SMOTEbig data analyticsclimate changedeep learningevolving datamultivariate time seriesnatural fire disastersnearmissreal-time data samplingsensorsunder-sampling

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.2K

Related Experiment Videos

Last Updated: May 28, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.2K

Area of Science:

  • Environmental Science
  • Data Science
  • Geospatial Analysis

Background:

  • Natural disasters, including forest fires, pose significant threats due to seasonal and anthropogenic factors.
  • Forest fires are increasing in frequency and destruction, impacting ecosystems and economies.
  • Predictive modeling for forest fires is challenged by highly imbalanced datasets (over 100,000 non-fire events per fire event).

Purpose of the Study:

  • To introduce a novel data sampling technique, Spatio-Temporal Agnostic Sampling (STAS), for handling imbalanced time-series data in forest fire prediction.
  • To provide a mathematical framework and complexity analysis for STAS, comparing it with existing methods like NearMiss and SMOTE.
  • To evaluate the effectiveness of STAS in improving forest fire probability classification and severity assessment models.

Main Methods:

  • Development and mathematical formulation of the Spatio-Temporal Agnostic Sampling (STAS) framework.
  • Complexity analysis comparing STAS against NearMiss and SMOTE.
  • Implementation of binary classification and regression models using STAS-generated data for fire prediction and severity assessment.
  • Extensive validation through 432 experiments and additional temporal data split analysis.

Main Results:

  • STAS demonstrates superior performance in handling imbalanced multivariate seasonal time-series data.
  • Binary classification models built on STAS data achieved an F1-score > 0.9 in 180 out of 216 experiments.
  • Regression models for fire severity assessment achieved an R2-score > 0.75 in 150 out of 216 experiments.

Conclusions:

  • The Spatio-Temporal Agnostic Sampling (STAS) framework is highly effective for improving forest fire prediction models.
  • STAS successfully addresses the challenge of highly imbalanced data in seasonal, multivariate time-series datasets.
  • The validated performance of STAS indicates its significant applicability for real-world forest fire early warning systems.