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

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...

You might also read

Related Articles

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

Sort by

Same author

Dysferlin stabilizes membrane nanodomains of cardiomyocytes after myocardial infarction.

Scientific reports·2026

Same author

Versatile Microfluidics Platform for Enhanced Multitarget Super-Resolution Microscopy.

ACS nano·2026

Same author

Impaired Atrial Mitochondrial Calcium Handling in Patients With Atrial Fibrillation.

Circulation research·2025

Same author

Reference the seabed topographic depth observations based on the national mean dynamic topography model.

MethodsX·2024

Same author

Pausing the Pandemic: Understanding and Managing Traveler and Community Spread of COVID-19 in Hawaii.

Transportation research record·2023

Same author

Vulnerabilities and risk perceptions of contracting COVID-19 among Nepali migrant workers.

Social sciences & humanities open·2023

Search research articles

Related Experiment Video

Updated: Jul 26, 2025

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

Assessing surface water pollution in Hanoi, Vietnam, using remote sensing and machine learning algorithms.

Thi-Nhung Do¹, Diem-My Thi Nguyen¹, Jiwnath Ghimire²

¹Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi, 334 Nguyen Trai, Thanh Xuan, Hanoi, Vietnam.

Environmental Science and Pollution Research International

|June 15, 2023

Summary

A new machine learning model (ML-CB) effectively estimates surface water pollutants like total suspended sediments (TSS), chemical oxygen demand (COD), and biological oxygen demand (BOD) using satellite data. This offers a vital tool for urban water quality monitoring.

Keywords:

Hanoi City Machine learning Remote sensing Surface water pollution Water quality parameters

More Related Videos

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

Related Experiment Videos

Last Updated: Jul 26, 2025

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

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

Area of Science:

Environmental Science
Remote Sensing
Machine Learning

Background:

Rapid urbanization worldwide, particularly in the Global South, has led to significant land-use changes and threats to surface water bodies.
Hanoi, Vietnam, experiences chronic surface water pollution, necessitating advanced monitoring methods.
Effective management of water resources requires improved tracking and analysis of pollutants.

Purpose of the Study:

To introduce and evaluate a novel machine learning model (ML-CB) for estimating key surface water pollutants.
To assess the efficacy of combining optical and RADAR satellite data for water quality monitoring.
To provide an alternative method for water quality assessment for urban planners and managers.

Main Methods:

Developed a machine learning model with the cubist algorithm (ML-CB).
Integrated optical (Sentinel-2A) and RADAR (Sentinel-1A) satellite imagery for pollutant estimation.
Validated model predictions against field survey data using regression analysis.

Main Results:

The ML-CB model demonstrated significant accuracy in estimating surface water pollutants.
The integration of optical and RADAR data proved effective for water quality assessment.
Predictive estimates of total suspended sediments (TSS), chemical oxygen demand (COD), and biological oxygen demand (BOD) were achieved.

Conclusions:

The ML-CB model offers a viable and effective alternative for surface water quality monitoring.
This approach can support sustainable water resource management in urban areas, especially in the Global South.
The study highlights the potential of combining machine learning and earth observation for environmental management.