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

Classification of Systems-I01:26

Classification of Systems-I

225
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
225
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

155
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:
155
Classification of Systems-II01:31

Classification of Systems-II

186
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
186
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

134
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
134
Classification of Signals01:30

Classification of Signals

566
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
566
Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

119
Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
119

You might also read

Related Articles

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

Sort by
Same author

EAACI Guidelines on Environmental Science for Allergy and Asthma-Evidence-Based Recommendations for Prevention and Public Health Action to Mitigate the Impact of Pollen Exposure on Respiratory Allergy.

Allergy·2026
Same author

Association of Plasma IL-6 with Indoor Radon Exposure in Children with Non-Allergic Asthma.

Journal of personalized medicine·2026
Same author

Risk and Protective Factors for Infection, Severe Disease, and Mortality in Epidemic Respiratory Viruses.

Allergy·2026
Same author

Molecular mechanisms of air pollution-induced carcinogenesis and the emerging role of microplastics.

Human genomics·2025
Same author

Distinct DNA methylation in mother-infant dyads exposed to PM2.5 in pregnancy.

Clinical epigenetics·2025
Same author

IL-4 variant and school mouse exposure increases asthma morbidity in urban schoolchildren.

The Journal of allergy and clinical immunology·2025

Related Experiment Video

Updated: Jul 30, 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

56

A multi-modal wildfire prediction and early-warning system based on a novel machine learning framework.

Rohan T Bhowmik1, Youn Soo Jung2, Juan A Aguilera2

  • 1Stanford University School of Medicine, Stanford, CA, 94305, USA; The Harker School, San Jose, CA, 95129, USA.

Journal of Environmental Management
|May 14, 2023
PubMed
Summary

A new machine learning system uses spatio-temporal data to predict wildfires, offering early warnings for vulnerable populations and improving fire management. This advanced wildfire prediction technology enhances safety and reduces environmental and economic damages.

Keywords:
Artificial intelligenceDisaster predictionMachine learningRemote sensor networkWildfire

More Related Videos

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
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

13.4K

Related Experiment Videos

Last Updated: Jul 30, 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

56
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
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

13.4K

Area of Science:

  • Environmental Science
  • Computer Science
  • Data Science

Background:

  • Wildfires pose increasing environmental and health risks, exacerbated by climate change and inadequate early warning systems.
  • The 2020-2021 California wildfires burned more acres than the previous century combined, highlighting the urgent need for improved prediction and response.
  • Existing systems fail to adequately protect vulnerable populations, widening inequality gaps.

Purpose of the Study:

  • To develop a multi-modal wildfire prediction and early warning system using a novel spatio-temporal machine learning architecture.
  • To create a comprehensive wildfire database integrating historical data, environmental/meteorological data, and geological information.
  • To identify and utilize leading and trailing indicators for wildfire risk assessment and propagation analysis.

Main Methods:

  • A comprehensive wildfire database (37M+ data points) was constructed, incorporating historical wildfire records, EPA sensor data, and geological information.
  • Data was augmented into 2.53 km x 2.53 km grids to address sensor network limitations.
  • A novel U-Convolutional Long Short-Term Memory (ULSTM) neural network was developed to capture spatio-temporal wildfire dynamics.

Main Results:

  • The ULSTM network achieved >97% accuracy in predicting 2018 wildfires, significantly outperforming traditional Convolutional Neural Network (CNN) methods (∼76%).
  • A retrospective study of 2018-2022 wildfire seasons demonstrated the model's ability to predict 85.7% of wildfires exceeding 300,000 acres.
  • Leading and trailing indicators were identified and tested, enhancing wildfire risk assessment and propagation prediction.

Conclusions:

  • The developed multi-modal wildfire prediction system offers a significant advancement over existing methods.
  • This technology can enable proactive wildfire prevention, provide crucial early warnings, and mitigate environmental and economic damages.
  • The ULSTM architecture demonstrates high efficacy in capturing complex spatio-temporal patterns for accurate wildfire forecasting.