# Synthetic Data Augmentation for Enhancing Harmful Algal Bloom Detection with Machine Learning

arXiv: https://arxiv.org/abs/2503.03794 · HTML: https://mlsanity.com/paper/2503.03794/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 5 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Stabilizing Generative Adversarial Networks: A Survey](https://mlsanity.com/paper/1910.00927/) (2019)
- [Data Augmentation in Earth Observation: A Diffusion Model Approach](https://mlsanity.com/paper/2406.06218/) (2024)
- [Conditional Generative Adversarial Networks to Model Urban Outdoor Air Pollution](https://mlsanity.com/paper/2010.02244/) (2020)
- [Towards Generating Large Synthetic Phytoplankton Datasets for Efficient Monitoring of Harmful Algal Blooms](https://mlsanity.com/paper/2208.02332/) (2022)
- [Hybrid Machine Learning techniques in the management of harmful algal blooms impact](https://mlsanity.com/paper/2402.09271/) (2024)
- [Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts](https://mlsanity.com/paper/2411.14896/) (2024)
- [HABNet: Machine Learning, Remote Sensing Based Detection and Prediction of Harmful Algal Blooms](https://mlsanity.com/paper/1912.02305/) (2019)
- [A feature-supervised generative adversarial network for environmental monitoring during hazy days](https://mlsanity.com/paper/2008.01942/) (2020)

## Abstract (arXiv)

Harmful Algal Blooms (HABs) pose severe threats to aquatic ecosystems and public health, resulting in substantial economic losses globally. Early detection is crucial but often hindered by the scarcity of high-quality datasets necessary for training reliable machine learning (ML) models. This study investigates the use of synthetic data augmentation using Gaussian Copulas to enhance ML-based HAB detection systems. Synthetic datasets of varying sizes (100-1,000 samples) were generated using relevant environmental featuresunicodex2015water temperature, salinity, and UVB radiationunicodex2015with corrected Chlorophyll-a concentration as the target variable. Experimental results demonstrate that moderate synthetic augmentation significantly improves model performance (RMSE reduced from 0.4706 to 0.1850; p < 0.001). However, excessive synthetic data introduces noise and reduces predictive accuracy, emphasizing the need for a balanced approach to data augmentation. These findings highlight the potential of synthetic data to enhance HAB monitoring systems, offering a scalable and cost-effective method for early detection and mitigation of ecological and public health risks.