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

Linear Approximations01:23

Linear Approximations

For a differentiable function of two variables, linear approximation estimates values near a known point by replacing the curved surface with its tangent plane. Consider the function\begin{equation*}f(x,y)=x^2+3y^2\end{equation*}near the point (2, 1). The exact value at this point is f(2, 1) = 22 + 3(1)2 = 4 + 3 = 7.The linear approximation of f(x, y)) near (a, b) is\begin{equation*}L(x,y)=f(a,b)+f_x(a,b)(x-a)+f_y(a,b)(y-b)\end{equation*}First, compute the partial derivatives: fx(x, y) = 2x and...

You might also read

Related Articles

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

Sort by
Same author

Laboratory based quantitative scoring system for pre-analytical quality indicators in arterial blood gas analysis.

Advances in laboratory medicine·2026
Same author

Compounded effects on wetland greenhouse gas fluxes from climate change and water management along a saline to freshwater gradient.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Beyond Mimicking Enzymes: NewTAML/Peroxide Abstracts sp<sup>3</sup> C-H Bonds to Initiate Biotranscendent Water-Purifying Mineralization of Fluoroquinolone Antibiotics.

Journal of the American Chemical Society·2025
Same author

Current protected areas provide limited benefits for European river biodiversity.

Nature communications·2025
Same author

River metabolism in the contiguous United States: A West of extremes.

Science (New York, N.Y.)·2025
Same author

Environmental effects of the Kakhovka Dam destruction by warfare in Ukraine.

Science (New York, N.Y.)·2025

Related Experiment Video

Updated: Jun 8, 2026

Laboratory-determined Phosphorus Flux from Lake Sediments as a Measure of Internal Phosphorus Loading
10:49

Laboratory-determined Phosphorus Flux from Lake Sediments as a Measure of Internal Phosphorus Loading

Published on: March 6, 2014

17.7K

Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework.

Longzhu Q Shen1,2, Giuseppe Amatulli3,4, Tushar Sethi2

  • 1University of Cambridge, Department of Zoology, Cambridge, CB2 3EJ, UK.

Scientific Data
|May 30, 2020
PubMed
Summary

Excessive nitrogen and phosphorus cause aquatic pollution and eutrophication. This study introduces a new geo-dataset mapping these nutrient concentrations across US streams using machine learning models.

More Related Videos

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.3K
Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
12:50

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

11.7K

Related Experiment Videos

Last Updated: Jun 8, 2026

Laboratory-determined Phosphorus Flux from Lake Sediments as a Measure of Internal Phosphorus Loading
10:49

Laboratory-determined Phosphorus Flux from Lake Sediments as a Measure of Internal Phosphorus Loading

Published on: March 6, 2014

17.7K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.3K
Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
12:50

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

11.7K

Area of Science:

  • Environmental Science
  • Water Quality Monitoring
  • Geospatial Analysis

Background:

  • Nitrogen (N) and Phosphorus (P) are vital nutrients for aquatic ecosystems but excessive amounts lead to pollution.
  • Eutrophication, a widespread issue caused by nutrient imbalance, is primarily driven by human activities.
  • Anthropogenic nutrient loading significantly impacts water body health and ecological processes.

Purpose of the Study:

  • To develop a high-resolution geo-dataset for estimating and mapping N and P concentrations in US streams.
  • To provide a comprehensive spatial understanding of nutrient distribution and potential pollution hotspots.
  • To support environmental management and policy decisions regarding water quality.

Main Methods:

  • Utilized Random Forest (RF), a machine learning algorithm, for predictive modeling.
  • Integrated data from 62,495 US stream monitoring stations (1994-2018).
  • Employed 47 near-globally available environmental variables for model development and spatial analysis.

Main Results:

  • Generated a geo-dataset with N and P concentrations at a ~1km spatial resolution for the conterminous US.
  • Validated seasonal models using internal and external procedures, achieving an average Pearson Coefficient of ~0.66.
  • Successfully mapped the spatial distribution of various chemical forms of N and P in US streams.

Conclusions:

  • The new geo-dataset provides valuable insights into nutrient pollution dynamics in US streams.
  • Machine learning models effectively estimate and map N and P concentrations, aiding in eutrophication assessment.
  • This resource can inform targeted interventions to mitigate nutrient pollution and improve water quality.