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 Experiment Video

Updated: Sep 16, 2025

Flow Cytometric Analysis of Particle-bound Bet v 1 Allergen in PM10
10:42

Flow Cytometric Analysis of Particle-bound Bet v 1 Allergen in PM10

Published on: November 19, 2016

7.7K

An ODE based neural network approach for PM2.5 forecasting.

Md Khalid Hossen1,2,3, Yan-Tsung Peng4, Asher Shao5

  • 1Social Networks and Human-Centered Computing, TIGP, Academia Sinica, Taipei, 115, Taiwan. kt.hossen27@iis.sinica.edu.tw.

Scientific Reports
|July 10, 2025
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Improving AI object detection in fire scenes through data augmentation.

Journal of occupational and environmental hygiene·2025
Same author

Enhancing PM2.5 prediction by mitigating annual data drift using wrapped loss and neural networks.

PloS one·2025
Same author

Anomalous behavior recognition of underwater creatures using lite 3D full-convolution network.

Scientific reports·2023
Same author

Using satellite data on remote transportation of air pollutants for PM2.5 prediction in northern Taiwan.

PloS one·2023
Same author

Influence of land-sea breeze on PM[Formula: see text] prediction in central and southern Taiwan using composite neural network.

Scientific reports·2023
Same author

BANet: A Blur-Aware Attention Network for Dynamic Scene Deblurring.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2022
Same journal

A tri-axis optomechanical accelerometer with plasmonic MIM waveguide and structural direction-dependent optical signatures.

Scientific reports·2026
Same journal

Holographic leaky-wave antennas with independently controlled multiple counter-rotating vortex beams.

Scientific reports·2026
Same journal

Differential associations of longitudinal hearing and vision trajectories with dementia and mild cognitive impairment in older adults.

Scientific reports·2026
Same journal

Abdominal obesity and leisure-time sedentary behavior in relation to gastroesophageal reflux disease risk: a prospective cohort study from the UK Biobank.

Scientific reports·2026
Same journal

Effect of nitrogen-rich COF incorporation on the structure and separation performance of polyamide nanofiltration membranes.

Scientific reports·2026
Same journal

Withanolide A inhibits hIAPP aggregation: An In silico, biophysical, and drosophila-based In vivo validation.

Scientific reports·2026
See all related articles

Advanced neural networks improve PM2.5 forecasting. New ordinary differential equation (ODE) models offer enhanced accuracy over traditional methods like Long Short-Term Memory (LSTM) for predicting air quality. These models show significant performance advantages in hourly forecasting.

Area of Science:

  • Environmental Science
  • Computer Science
  • Data Science

Background:

  • Accurate PM2.5 forecasting is crucial but challenging due to complex influencing factors.
  • Traditional deep learning models like LSTM and BiLSTM face limitations in long-term time-series prediction accuracy.
  • Recurrent Neural Networks (RNNs) struggle with long-term dependencies and scalability.

Purpose of the Study:

  • To develop advanced neural network models for more accurate time-series prediction of PM2.5 concentrations.
  • To address the limitations of existing models in handling complex dynamics and long-term dependencies.
  • To propose and evaluate novel Ordinary Differential Equation (ODE)-based models for improved PM2.5 forecasting.

Main Methods:

  • Proposed two ODE-based models: a transformer-based ODE model and a closed-form ODE model.

More Related Videos

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
09:33

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

2.4K
Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions
05:45

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

Published on: January 7, 2019

10.8K

Related Experiment Videos

Last Updated: Sep 16, 2025

Flow Cytometric Analysis of Particle-bound Bet v 1 Allergen in PM10
10:42

Flow Cytometric Analysis of Particle-bound Bet v 1 Allergen in PM10

Published on: November 19, 2016

7.7K
Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
09:33

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

2.4K
Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions
05:45

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

Published on: January 7, 2019

10.8K
  • Utilized continuous-time neural networks and differential equations for time-series modeling.
  • Conducted empirical evaluations and paired t-tests to compare model performance against LSTM-based models.

    • Main Results:

    • The proposed ODE-based models significantly enhanced prediction accuracy compared to LSTM-based models.
    • Improvements in prediction accuracy ranged from 2.91% to 14.15% for 1-hour to 8-hour predictions.
    • The proposed model (CCCFC) demonstrated a statistically significant performance advantage over BiLSTM, LSTM, GRU, ODE-LSTM, and PCNN,CNN-LSSTM.

    Conclusions:

    • ODE-based models, particularly closed-form networks, offer superior scalability and accuracy for PM2.5 time-series forecasting.
    • The developed models provide a robust alternative to traditional deep learning approaches for complex environmental data.
    • The findings reinforce the effectiveness of the proposed CCCFC model for hourly PM2.5 forecasting.