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

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

7.4K
This protocol was designed to train a machine learning algorithm to use a combination of imaging parameters derived from magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a rat model of breast cancer bone metastases to detect early metastatic disease and predict subsequent progression to...
7.4K
Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures09:11

Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures

3.5K
Here, we present a protocol to investigate the impacts of hydraulic fracturing on nearby streams by analyzing their water and sediment microbial communities.
3.5K
Constructing and Visualizing Models using Mime-based Machine-learning Framework06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

2.3K
Mime is a flexible computational framework to construct a machine learning-based integration model with elegant performance. Here, we provide a detailed step-by-step procedure for developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with disease progression, patient outcomes, and therapeutic response.
2.3K
The Colonization of Land02:22

The Colonization of Land

37.3K
Changes in the environment of the early Earth drove the evolution of organisms. As prokaryotic organisms in the oceans began to photosynthesize, they produced oxygen. Eventually, oxygen saturated the oceans and entered the air, resulting in an increase in atmospheric oxygen concentration, known as the oxygen revolution approximately 2.3 billion years ago. Therefore, organisms that could use oxygen for cellular respiration had an advantage. More than 1.5 years ago, eukaryotic cells and...
37.3K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

7.9K
This methodology produces decision trees that target population groups more prone to suffering from mild cognitive impairment and are useful for cost-effective selective screening of the...
7.9K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

940
This study employed voice signal analysis and machine learning methods, utilizing MATLAB to extract distinctive voice features for non-invasive early detection of asthma. The Support Vector Machine (SVM) and Random Forest (RF) algorithms demonstrated comparable performance in terms of overall classification accuracy, although SVM may achieve a better balance between sensitivity and...
940

You might also read

Related Articles

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

Sort by
Same author

Culvert Retrofit with Green Filter Media for the Removal of Phosphorus from Stormwater Runoff.

Materials (Basel, Switzerland)·2026
Same author

Integrating data driven approaches and geospatial modeling to identify environmental hot spots and guide targeted restoration efforts.

Journal of environmental management·2026
Same author

Modeling carbon dynamics from a heterogeneous watershed in the mid-Atlantic USA: A distributed-calibration and independent verification (DCIV) approach.

The Science of the total environment·2024
Same author

SNAPSHOT USA 2021: A third coordinated national camera trap survey of the United States.

Ecology·2024
Same author

Repurposing spent biomass of vetiver grass used for stormwater treatment to generate biochar and ethanol.

Chemosphere·2024
Same author

Crop production and water quality under 1.5 °C and 2 °C warming: Plant responses and management options in the mid-Atlantic region.

The Science of the total environment·2023

Related Experiment Video

Updated: Jan 20, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.4K

Evaluating the impact of land uses on stream integrity using machine learning algorithms.

Subhasis Giri1, Zhen Zhang2, Daryl Krasnuk1

  • 1Department of Ecology, Evolution, and Natural Resources, School of Environmental and Biological Sciences, Rutgers, The State University of New Jersey, New Brunswick NJ-08901, USA.

The Science of the Total Environment
|August 30, 2019
PubMed
Summary

Urban land use negatively impacts aquatic ecosystems. This study used machine learning to identify specific land use thresholds that degrade stream biological integrity, aiding watershed management.

Keywords:
Boosted regression treesEco-hydrologyMachine learning algorithmsMacroinvertebrate indexPartial dependence plotRandom forestsRelative influenceSpearman correlation matrixStream integrity

More Related Videos

Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures
09:11

Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures

Published on: April 4, 2021

3.5K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.3K

Related Experiment Videos

Last Updated: Jan 20, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.4K
Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures
09:11

Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures

Published on: April 4, 2021

3.5K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.3K

Area of Science:

  • Environmental Science
  • Ecology
  • Hydrology

Background:

  • Declining aquatic ecological integrity is linked to urban land use globally.
  • Understanding specific urban land use impacts and thresholds is crucial for watershed management.

Purpose of the Study:

  • To model stream impairment using macroinvertebrate data in an urbanizing watershed.
  • To evaluate the effectiveness of Random Forests (RF) and Boosted Regression Trees (BRT) for predicting stream integrity based on land use.
  • To identify cumulative land use thresholds affecting aquatic ecosystems.

Main Methods:

  • Employed Random Forests (RF) and Boosted Regression Trees (BRT) machine learning algorithms.
  • Modeled stream impairment using the High Gradient Macroinvertebrate Index (HGMI).
  • Analyzed watershed land use/land cover data, including Impervious Surface cover (ISC).

Main Results:

  • Machine learning models explained over 50% of stream integrity variability.
  • High-medium density urban (>30% ISC), low density urban (15-30% ISC), and transitional/barren land negatively impacted stream integrity.
  • Identified critical thresholds: stream integrity declined sharply when high-medium urban land exceeded 10%, low-density urban 8%, and transitional/barren land 2%.

Conclusions:

  • RF models were robust and easier to train than BRT.
  • Specific land use types and their cumulative thresholds significantly influence stream biological integrity.
  • Identified thresholds provide objective criteria for land use zoning and restoration planning.