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

Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

520
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
520
Light Acquisition02:16

Light Acquisition

8.5K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.5K
Regression Analysis01:11

Regression Analysis

5.8K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.8K
Multiple Regression01:25

Multiple Regression

3.1K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.1K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.5K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.5K

You might also read

Related Articles

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

Sort by
Same author

Distribution and influencing factors of soil and groundwater pollution at pharmaceutical sites in China.

Journal of environmental sciences (China)·2026
Same author

Novel photoelectron-driven nitrate reduction in anammox granules using photosensitive semiconductor iron mineral for wastewater treatment.

Water research·2026
Same author

Water Quality and Hydrogeochemical Mechanisms of River Water in the Strong Runoff Piedmont Region: Insight From Hydrochemical and Isotopic Evidence.

Water environment research : a research publication of the Water Environment Federation·2026
Same author

Sunlight-activated semiconducting iron minerals in anammox granules: Stimulating metabolic cooperation, suppressing nitrate accumulation, and mitigating oxidative stress.

Bioresource technology·2026
Same author

Decoding the microplastic Micro-interface: a complex Web of gene transfer and pathogenic threats in wastewater.

Environment international·2025
Same author

The coexistence of Bacillus haynesii and Bacillus tequilensis achieves synchronous algicidal activity and denitrification and enhances the MC-LR degradation of phycosphere microbiota.

Journal of hazardous materials·2025

Related Experiment Video

Updated: Jul 29, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

35

Prediction and sensitivity analysis of chlorophyll a based on a support vector machine regression algorithm.

Li Xu1,2, Guizhen Hao3,4, Simin Li1

  • 1School of Energy and Environmental Engineering, Hebei University of Engineering, Handan, 056038, China.

Environmental Monitoring and Assessment
|May 20, 2023
PubMed
Summary
This summary is machine-generated.

Total phosphorus (TP) is the main factor controlling phytoplankton blooms in the Qingshui River. This study developed a support vector machine regression model to predict chlorophyll a (Chl-a) and identify key environmental drivers.

Keywords:
Chlorophyll aEutrophicationSensitivity analysisWater environmental factors

More Related Videos

Author Spotlight: Non-Invasive High-Resolution Measurement of Chlorophyll Synthesis During De-Etiolation
07:58

Author Spotlight: Non-Invasive High-Resolution Measurement of Chlorophyll Synthesis During De-Etiolation

Published on: January 12, 2024

851
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

1.5K

Related Experiment Videos

Last Updated: Jul 29, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

35
Author Spotlight: Non-Invasive High-Resolution Measurement of Chlorophyll Synthesis During De-Etiolation
07:58

Author Spotlight: Non-Invasive High-Resolution Measurement of Chlorophyll Synthesis During De-Etiolation

Published on: January 12, 2024

851
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

1.5K

Area of Science:

  • Environmental Science
  • Water Quality Management
  • Ecological Modeling

Background:

  • Planktonic algae blooms degrade river water quality and are challenging to manage.
  • Understanding the influence of environmental factors on algal blooms is crucial for effective control strategies.

Purpose of the Study:

  • To develop a predictive model for chlorophyll a (Chl-a) concentrations using support vector machine regression (SVR).
  • To identify the key environmental factors influencing Chl-a levels and phytoplankton outbreaks in the Qingshui River.
  • To perform a sensitivity analysis of the Chl-a prediction model.

Main Methods:

  • Utilized a radial basis function kernel SVR model with tenfold cross-validation for parameter optimization.
  • Analyzed temporal and spatial variations of environmental factors including total nitrogen (TN), ammonia nitrogen (NH4+-N), total phosphorus (TP), dissolved oxygen (DO), pH, and water temperature (WT).
  • Conducted a sensitivity analysis to determine the contribution of each environmental factor to Chl-a levels.

Main Results:

  • The optimized SVR model demonstrated a good fit with low training (0.032) and verification (0.067) errors.
  • Total phosphorus (TP) exhibited the highest sensitivity coefficient (0.571) and contribution (33%) to Chl-a.
  • Dissolved oxygen (DO) and pH also showed significant sensitivity (0.28 and 0.243, respectively), while TN and NH4+-N had the lowest impact.

Conclusions:

  • Total phosphorus (TP) is identified as the primary limiting factor for Chl-a in the Qingshui River.
  • Controlling TP levels is the most effective strategy for preventing and managing phytoplankton outbreaks in the studied river system.