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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...

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Related Experiment Video

Updated: Nov 21, 2025

Preparation and Testing of Impedance-based Fluidic Biochips with RTgill-W1 Cells for Rapid Evaluation of Drinking Water Samples for Toxicity

Preparation and Testing of Impedance-based Fluidic Biochips with RTgill-W1 Cells for Rapid Evaluation of Drinking Water Samples for Toxicity

Published on: March 7, 2016

Water Quality Prediction Using Artificial Intelligence Algorithms.

Theyazn H H Aldhyani¹, Mohammed Al-Yaari², Hasan Alkahtani³

¹Community College of Abqaiq, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia.

Applied Bionics and Biomechanics

|January 18, 2021

Summary

This summary is machine-generated.

Advanced artificial intelligence (AI) models accurately predict water quality index (WQI) and water quality classification (WQC). Machine learning algorithms show high accuracy, aiding in effective water resource management and pollution control.

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Area of Science:

Environmental Science
Computer Science
Data Science

Background:

Water quality is increasingly threatened by diverse pollutants.
Accurate modeling and prediction of water quality are crucial for effective pollution control and water resource management.

Purpose of the Study:

To develop and evaluate advanced artificial intelligence (AI) algorithms for predicting water quality index (WQI) and water quality classification (WQC).
To assess the performance of various AI models in forecasting water quality parameters.

Main Methods:

Developed nonlinear autoregressive neural network (NARNET) and long short-term memory (LSTM) for WQI prediction.
Utilized support vector machine (SVM), K-nearest neighbor (K-NN), and Naive Bayes for WQC forecasting.
Evaluated models using a dataset with 7 significant parameters and statistical metrics.

Main Results:

NARNET slightly outperformed LSTM in WQI prediction, with regression coefficients of 96.17% and 94.21%, respectively.
SVM achieved the highest accuracy (97.01%) for WQC prediction.
All developed models demonstrated superior robustness and accuracy in predicting and classifying water quality.

Conclusions:

Advanced AI algorithms, including NARNET, LSTM, and SVM, are highly effective for water quality prediction and classification.
This research provides a robust framework for enhancing water quality monitoring and management strategies.
The findings contribute significantly to the field of intelligent water resource management.