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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...

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

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Published on: February 25, 2021

Real-time eutrophication status evaluation of coastal waters using support vector machine with grid search algorithm.

Xianyu Kong¹, Yuyan Sun¹, Rongguo Su¹

¹Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education Ocean University of China, Qingdao 266100, China.

Marine Pollution Bulletin

|April 25, 2017

Summary

A new method using a GS optimized Support Vector Machine (SVM) effectively models coastal water eutrophication. This approach accurately predicts the TRIX index using easily measured parameters, aiding marine health management.

Keywords:

CDOM Easily measured parameters Eutrophication assessment Fluorescence Support vector machine TRIX

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Area of Science:

Marine Science
Environmental Monitoring
Data Science

Background:

Real-time monitoring of coastal water eutrophication is crucial for cost-effective management and timely marine health advisories.
Existing methods may be resource-intensive, necessitating simpler, rapid assessment techniques.

Purpose of the Study:

To develop and validate a Support Vector Machine (SVM) model for rapid assessment of coastal water eutrophication status.
To establish relationships between easily measurable water quality parameters and the TRIX index.
To evaluate the model's predictive performance and classification accuracy.

Main Methods:

A Gradient Boosting (GS) optimized Support Vector Machine (SVM) was employed.
The model was trained and validated using six easily measured parameters (DO, Chl-a, C1, C2, C3, C4) and the TRIX index.
Statistical metrics including R-squared and classification accuracy were used for evaluation.

Main Results:

The GS-optimized SVM demonstrated strong predictive performance with R-squared values of 0.92 (training) and 0.91 (validation) at a 95% confidence level.
Classification accuracy for eutrophication status reached 86.5% (training) and 85.6% (validation).
The model successfully established reliable relationships between the selected parameters and the TRIX index.

Conclusions:

The developed SVM technique provides a feasible and accurate method for the timely evaluation of coastal water eutrophication.
This approach offers a cost-effective solution for marine health management programs.
Utilizing easily measured parameters enhances the practicality of real-time eutrophication monitoring.