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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
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...
56
Survival Tree01:19

Survival Tree

87
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...
87

You might also read

Related Articles

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

Sort by
Same author

Explainable machine learning for flood susceptibility mapping in Kyrgyzstan's major urban areas.

Scientific reports·2026
Same author

Nickel and Cobalt Accumulation and Human Health Risk in Guava (Psidium guajava L.) Under Wastewater Irrigation: A Soil-Plant Transfer Assessment.

Bulletin of environmental contamination and toxicology·2026
Same author

Retraction Note: Effects of organic and chemical fertilizers on the growth, heavy metal/metalloid accumulation, and human health risk of wheat (Triticum aestivum L.).

Environmental science and pollution research international·2026
Same author

Multifunctional carbon dot-based sensors for smart food packaging applications.

Critical reviews in food science and nutrition·2026
Same author

Potentially Toxic Metal Flux and Bioaccumulation Pathways in Buffaloes from Rural and Urban Agroecosystems: a Multi-Matrix Environmental Health Assessment.

Environmental geochemistry and health·2026
Same author

From roadside soil to cow milk: a potentially toxic metal transfer study using Pennisetum glaucum as fodder.

Environmental monitoring and assessment·2026

Related Experiment Video

Updated: Jul 9, 2025

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

13.3K

Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization

Adil Masood1, Mohammed Majeed Hameed2, Aman Srivastava3

  • 1Department of Civil Engineering, Jamia Millia Islamia University, New Delhi, India.

Scientific Reports
|November 29, 2023
PubMed
Summary

This study introduces the ELM-SO model for predicting fine particulate matter (PM2.5) concentrations. The novel hybrid approach demonstrated superior accuracy compared to other machine learning models, offering a valuable tool for air quality forecasting.

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

1.4K
Assessing the Particulate Matter Removal Abilities of Tree Leaves
05:07

Assessing the Particulate Matter Removal Abilities of Tree Leaves

Published on: October 7, 2018

6.8K

Related Experiment Videos

Last Updated: Jul 9, 2025

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

13.3K
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

1.4K
Assessing the Particulate Matter Removal Abilities of Tree Leaves
05:07

Assessing the Particulate Matter Removal Abilities of Tree Leaves

Published on: October 7, 2018

6.8K

Area of Science:

  • Environmental Science
  • Computer Science
  • Public Health

Background:

  • Fine particulate matter (PM2.5) poses significant health risks and causes premature mortality in urban areas like Delhi.
  • Accurate PM2.5 concentration forecasting is crucial for public health advisories and awareness.

Purpose of the Study:

  • To develop and evaluate a novel hybrid model, Extreme Learning Machine with Snake Optimization (ELM-SO), for PM2.5 concentration forecasting.
  • To compare the predictive performance of the ELM-SO model against established machine learning and deep learning models.

Main Methods:

  • A hybrid ELM-SO model was developed using air quality and meteorological data.
  • The ELM-SO model's performance was benchmarked against Support Vector Regression (SVR), Random Forest (RF), Extreme Learning Machines (ELM), Gradient Boosting Regressor (GBR), XGBoost, and Long Short-Term Memory (LSTM) networks.

Main Results:

  • The ELM-SO model achieved the highest predictive accuracy.
  • The ELM-SO model demonstrated a testing R-squared value of 0.928 and a root mean square error of 30.325 µg/m³.

Conclusions:

  • The ELM-SO technique is a highly effective tool for accurate PM2.5 forecasting.
  • This advanced forecasting capability can improve understanding and anticipation of air pollution's impact on health and the environment.