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Related Experiment Video

Updated: Jul 17, 2025

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

Influence of resampling techniques on Bayesian network performance in predicting increased algal activity.

Maryam Zeinolabedini Rezaabad¹, Heather Lacey², Lucy Marshall³

¹Water Research Centre, School of Civil and Environmental Engineering, University of New South Wales, Kensington, New South Wales, Australia; ARC Training Centre Data Analytics for Resources and Environments, School of Life and Environmental Sciences, The University of Sydney, Camperdown, New South Wales, Australia.

|September 4, 2023

Summary

Resampling techniques like SMOTE improve algal bloom prediction by balancing imbalanced datasets. This enhances early warning systems for aquatic health and human safety.

Keywords:

Bayesian network Chlorophyll-a Imbalanced data Over-sampling Under-sampling Water quality

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Area of Science:

Environmental Science
Data Science
Ecology

Background:

Predicting algal blooms is crucial for aquatic and human health.
Imbalanced datasets with infrequent severe blooms hinder accurate prediction models.

Purpose of the Study:

Investigate resampling techniques to address imbalanced data for algal activity prediction.
Determine if resampling improves the prediction accuracy of algal blooms.

Main Methods:

Applied Kmeans under-sampling (US_Kmeans), SMOTE, and SCUT to a Bayesian network (BN) model.
Used the BN model to predict chlorophyll-a (chl-a) concentrations in Lake Burragorang, Australia.
Evaluated model performance using true positive rate (TPR) and area under the curve (AUC).

Main Results:

SMOTE generated synthetic data that resulted in the best BN graphical structure.
All resampling techniques improved the BN's ability to detect high algal activity events.
Resampling enhanced the prediction of increased algal activity, indicated by higher chl-a.

Conclusions:

Resampling techniques effectively address imbalanced data in algal bloom prediction.
SMOTE shows promise for improving the accuracy of predictive models for algal activity.
Findings guide future data collection and model development for water quality management.