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Predicting Algal Bloom Dynamics in Drinking Water Reservoirs Using High-Frequency In Situ Data and Machine Learning.

Jiangbin Wang^1,2, Min Jiang¹, Shuhua Wang¹

¹School of Resources and Environmental Sciences, Quanzhou Normal University, Quanzhou 362000, China.

|May 26, 2026

Summary

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This summary is machine-generated.

An optimized Transformer model accurately predicts algal abundance in drinking water reservoirs 24 hours ahead. This advancement aids in early warning systems and effective management of algal blooms.

Area of Science:

Environmental Science
Water Resource Management
Data Science

Background:

Algal proliferation in subtropical drinking water reservoirs poses a significant risk to water quality and management.
High-frequency in situ data is crucial for developing reliable algal abundance prediction models for early warning systems.

Purpose of the Study:

To analyze interannual variations in algal abundance in Shanmei Reservoir using high-frequency data (2020-2025).
To forecast algal abundance 24 hours ahead using an optimized Transformer model.
To identify key environmental factors influencing algal dynamics.

Main Methods:

Analysis of high-frequency in situ data from Shanmei Reservoir (2020-2025).
Application of an optimized Transformer model for 24-hour algal abundance forecasting.

Keywords:

SHAP drinking water reservoirs optimized Transformer model phytoplankton abundance

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Utilized SHapley Additive exPlanations (SHAPs) for feature importance analysis.

Main Results:

Algal abundance in Shanmei Reservoir showed a consistent increase, doubling from 2021 to 2025.
The optimized Transformer model achieved a high predictive performance (R² = 0.88) for hourly algal abundance.
Key predictors for algal dynamics included prior algal abundance, permanganate index, dissolved oxygen, air temperature, wind speed, and pH.

Conclusions:

An optimized independent learning model integrating multi-scale features significantly enhances algal dynamics prediction.
The developed model provides a robust technical foundation for early algal bloom warning and refined reservoir management.
Understanding seasonal variations in environmental factors like pH and total nitrogen is vital for reservoir management.