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

Testing Water Quality

Testing Water Quality

When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...

Quality of Water

Quality of Water

In concrete preparation, the quality of water is paramount as it affects the strength and durability of the concrete. Potable water is usually preferred; however, it must not have excessive sodium or potassium to prevent compromising the concrete's integrity. Water quality is typically evaluated based on impurities such as dissolved solids, chlorides, and sulfates, and its pH value is ideally between 6 and 8. Even slightly acidic natural water may be acceptable unless it contains harmful...

Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

Key Elements for Plant Nutrition

Key Elements for Plant Nutrition

Like all living organisms, plants require organic and inorganic nutrients to survive, reproduce, grow and maintain homeostasis. To identify nutrients that are essential for plant functioning, researchers have leveraged a technique called hydroponics. In hydroponic culture systems, plants are grown—without soil—in water-based solutions containing nutrients. At least 17 nutrients have been identified as essential elements required by plants. Plants acquire these elements from the atmosphere, the...

Sampling Plans

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...

You might also read

Related Articles

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

Sort by

Same author

Microplastic transport in meandering open-channel flows: Coupled effects of sinuosity and particle density.

Journal of contaminant hydrology·2026

Same author

Human Progenitor T-Cell Differentiation Regulated by the Mechanical Resistance of Thymus-Mimetic Extracellular Matrices.

Advanced healthcare materials·2026

Same author

Water quality monitoring using hybrid physical-soft sensors for river digital twins: a comprehensive review.

Water science and technology : a journal of the International Association on Water Pollution Research·2025

Same author

Improved longevity and <i>in vivo</i> performance of neurotransmitter detection using 30 µm cone-shaped carbon fiber microelectrode.

Frontiers in bioengineering and biotechnology·2025

Same author

Human progenitor T-cell differentiation regulated by the mechanical resistance of thymus-mimetic extracellular matrices.

bioRxiv : the preprint server for biology·2025

Same author

Turbidity and suspended sediment relationship based on sediment composition and particle size distribution.

Scientific reports·2025

Search research articles

Related Experiment Video

Updated: Jun 17, 2026

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

A cluster-based virtual sensing framework for estimating total nitrogen and total phosphorus using sensor-measurable

Yumin Kang¹, Su Han Nam¹, Siyoon Kwon²

¹Department of Civil and Environmental Engineering, Myongji University, Yongin, South Korea.

Journal of Contaminant Hydrology

|June 15, 2026

Summary

This study introduces a cluster-based virtual sensing framework to improve nutrient estimation in large river basins. By grouping areas with similar water quality, it enhances the accuracy of total nitrogen (TN) and total phosphorus (TP) predictions.

Keywords:

Machine learning Spatial clustering Total nitrogen Total phosphorus Virtual sensing Water quality monitoring

More Related Videos

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds

Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds

Published on: November 13, 2017

Related Experiment Videos

Last Updated: Jun 17, 2026

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds

Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds

Published on: November 13, 2017

Area of Science:

Environmental Science
Water Resource Management
Machine Learning Applications

Background:

Spatial heterogeneity in large river basins complicates water-quality monitoring.
Existing single-model virtual-sensor approaches struggle with site-specific nutrient responses (TN, TP).

Purpose of the Study:

To develop a novel cluster-based virtual-sensing framework for improved nutrient estimation.
To enhance the spatial precision and interpretability of total nitrogen and total phosphorus monitoring.

Main Methods:

Applied Hierarchical Cluster Analysis (HCA), K-means, and Gaussian Mixture Model (GMM) for spatial clustering.
Developed independent machine learning virtual sensors (RF, XGB, MLP) for each cluster.
Utilized Water Quality Monitoring Network (WQMN) data from 2016-2025.

Main Results:

The cluster-based framework significantly improved R-squared values for TN (up to 0.045) and TP (up to 0.029) compared to no-clustering.
Improvements were most evident under restricted input conditions (e.g., excluding direct nutrient data).
The Random Forest (RF) model within the cluster framework demonstrated superior performance.

Conclusions:

Combining spatial clustering with machine learning virtual sensors enhances nutrient estimation accuracy in large river basins.
This approach improves spatial precision and interpretability for water quality monitoring.
The framework is particularly effective for managing water quality with limited input data.