Machine Learning Applications for Predicting Harmful Algal Blooms

Harmful algal blooms (HABs) pose serious threats to aquatic ecosystems, public health and coastal economies worldwide. Machine learning has emerged as a powerful approach to anticipate HAB onset by analysing complex interactions among environmental drivers such as sea surface temperature, nutrient loading, hydrodynamics and remote‐sensing imagery. Supervised learning methods—including decision trees, support vector machines and random forests—have been applied to in situ monitoring and satellite‐derived datasets to classify bloom presence and intensity. Deep‐learning architectures, notably convolutional neural networks (CNNs) and long short‐term memory (LSTM) networks, capture spatiotemporal patterns in multivariate data streams and forecast bloom trajectories days to weeks in advance. Ensemble learning and hybrid frameworks combine complementary algorithms to improve robustness and mitigate overfitting, while Bayesian or grid‐search strategies refine hyperparameter settings. Emerging work integrates novel data structures, such as spatiotemporal datacubes, and leverages transfer learning to overcome regional data scarcity. Together, these developments yield predictive tools that can inform shellfish harvesting advisories, guide monitoring campaigns and support early‐warning systems on a global scale.

Research from Nature Portfolio

A study developed a random forest model trained on molecular assays of the toxic dinoflagellate Alexandrium minutum in the NW Adriatic Sea to predict bloom occurrences. By linking environmental covariates—temperature, salinity and nutrient concentrations—with molecular presence–absence data, the model achieved over 80 per cent accurate classification of toxic events. This work demonstrates the utility of tree‐based algorithms in translating molecular monitoring into operational forecasts and highlights the potential for similar approaches in other coastal regions subject to paralytic shellfish poisoning.

Machine Learning Applications for Predicting Harmful Algal Blooms publication trend

The graph below shows the total number of articles in machine learning applications for predicting harmful algal blooms across all publications each year (not limited to Nature Index journals).

Technical terms

Random Forest: An ensemble of decision trees that combines multiple classifiers to improve predictive accuracy and control overfitting.

Convolutional Neural Network (CNN): A deep‐learning model that applies convolutional filters to capture spatial patterns in image or grid-based data.

Long Short-Term Memory (LSTM): A recurrent neural network architecture designed to learn long-range temporal dependencies in sequential data.

Gradient Boosting: A machine learning technique that builds an additive model by sequentially fitting new models to residual errors of prior models.

Ensemble Learning: A strategy that integrates multiple models to obtain better predictive performance than any individual model.

Spatiotemporal Datacube: A multidimensional data structure that organises observations across space and time for use in machine learning.

References

1. Hybrid machine learning techniques in the management of harmful algal blooms impact. Computers and Electronics in Agriculture (2023).
2. A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination. Journal of Marine Science and Engineering (2021).
3. A model predicting the PSP toxic dinoflagellate Alexandrium minutum occurrence in the coastal waters of the NW Adriatic Sea. Scientific Reports (2019).
4. Ensemble Machine Learning of Gradient Boosting (XGBoost, LightGBM, CatBoost) and Attention-Based CNN-LSTM for Harmful Algal Blooms Forecasting. Toxins (2023).
5. HABNet: Machine Learning, Remote Sensing-Based Detection of Harmful Algal Blooms. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2020).