Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Trial and Error and Algorithm

Trial and Error and Algorithm

A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Genetics of Speciation

Genetics of Speciation

Speciation is the evolutionary process resulting in the formation of new, distinct species—groups of reproductively isolated populations.

What is Population Genetics?

What is Population Genetics?

A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.

What is Genetic Engineering?

What is Genetic Engineering?

You might also read

Related Articles

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

Sort by

Same author

Correcting spatial transcriptomics data affected by a prevalent transcript leakage problem across platforms, species, and tissues.

bioRxiv : the preprint server for biology·2026

Same author

A Coral-Inspired Dual Modal Hydrogel Sensor with Deep Learning-Assisted Decoupling of Force-Thermal Stimuli.

ACS applied materials & interfaces·2026

Same author

Gut microbiota modulation in the prevention and treatment of heat stroke.

Frontiers in immunology·2026

Same author

Integrating high-throughput phenomics and GWAS unravels the HaCBF4-<i>HaHAK11</i> module to regulate salt stress tolerance in sunflower (<i>Helianthus annuus</i> L.).

Horticulture research·2026

Same author

Multimodal PCSC Sensors for Real-Time Temperature and Force Detection Using LRTNet.

Sensors (Basel, Switzerland)·2026

Same author

Elevating RNA m<sup>5</sup>C methylation provides a promising strategy for crop productivity.

National science review·2026

Same journal

Environmental aging behavior and organic matter release of biodegradable microplastics PLA, PBAT and PHBV under different coastal environmental conditions.

Marine pollution bulletin·2026

Same journal

Macroalgae detritus accelerates degradation of recalcitrant organic matter in coastal marine sediments.

Marine pollution bulletin·2026

Same journal

Three-dimensional decadal simulation of dissolved tracer dispersion in the Pacific Ocean and its marginal seas.

Marine pollution bulletin·2026

Same journal

A standardized method to monitor marine litter in submarine canyons: implementation in Atlantic and Mediterranean reef ecosystems.

Marine pollution bulletin·2026

Same journal

Distribution patterns of microplastics in seawater and sediment, and their bioaccumulation by Manila clam (Ruditapes philippinarum) in the coastal regions of Qingdao, China.

Marine pollution bulletin·2026

Same journal

Source and depositional pattern of bulk organic matter in a subtropical eco-sensitive lake sediment in Bihar, India: A Bayesian isotope mixing approach.

Marine pollution bulletin·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 4, 2026

Chemical Analysis of Water-accommodated Fractions of Crude Oil Spills Using TIMS-FT-ICR MS

Chemical Analysis of Water-accommodated Fractions of Crude Oil Spills Using TIMS-FT-ICR MS

Published on: March 3, 2017

Using a genetic algorithm to improve oil spill prediction.

Weijun Guo¹, Meirong Jiang², Xueyan Li³

¹College of Environmental Sciences and Engineering, Dalian Maritime University, Dalian 116026, China.

Marine Pollution Bulletin

|October 11, 2018

Summary

This summary is machine-generated.

A genetic algorithm (GA) optimized oil spill model parameters, reducing discrepancies between predicted and observed pollution. This approach enhances the accuracy of oil slick pattern prediction without manual tuning.

Keywords:

Genetic algorithm Model evaluation Oil spill Parameter optimisation

More Related Videos

Enhanced Oil Recovery using a Combination of Biosurfactants

Enhanced Oil Recovery using a Combination of Biosurfactants

Published on: June 3, 2022

Improved Polydimethylsiloxane (PDMS) Double Casting via Silicone Oil Treatment for Densely Packed Microstructure Replication

Improved Polydimethylsiloxane (PDMS) Double Casting via Silicone Oil Treatment for Densely Packed Microstructure Replication

Published on: July 18, 2025

Related Experiment Videos

Last Updated: Feb 4, 2026

Chemical Analysis of Water-accommodated Fractions of Crude Oil Spills Using TIMS-FT-ICR MS

Chemical Analysis of Water-accommodated Fractions of Crude Oil Spills Using TIMS-FT-ICR MS

Published on: March 3, 2017

Enhanced Oil Recovery using a Combination of Biosurfactants

Enhanced Oil Recovery using a Combination of Biosurfactants

Published on: June 3, 2022

Improved Polydimethylsiloxane (PDMS) Double Casting via Silicone Oil Treatment for Densely Packed Microstructure Replication

Improved Polydimethylsiloxane (PDMS) Double Casting via Silicone Oil Treatment for Densely Packed Microstructure Replication

Published on: July 18, 2025

Area of Science:

Environmental Science
Computational Modeling
Oceanography

Background:

Oil spill model performance is sensitive to parameter choices.
Manual parameter optimization is time-consuming and inefficient.

Purpose of the Study:

To evaluate a genetic algorithm (GA) for optimizing oil spill model parameters.
To improve the accuracy of Lagrangian oil particle models in predicting spill areas.

Main Methods:

Developed an evaluation function based on the percentage of coincidence between predicted and observed polluted areas.
Utilized a genetic algorithm (GA) to iteratively optimize model parameters.
Ran the oil spill model multiple times with continuously adjusted parameters to maximize the objective function.

Main Results:

The GA successfully reduced discrepancies between model predictions and real oil spill observations.
Optimized parameters led to reasonable accuracy in predicting oil slick patterns.
Multi-objective optimization across different time points further improved model performance.

Conclusions:

Genetic algorithms offer an efficient method for optimizing oil spill model parameters.
The optimized model demonstrates improved accuracy in predicting oil spill trajectories and areas.
This automated approach enhances the reliability of oil spill forecasting and response efforts.