Articles | Volume 16, issue 1
https://doi.org/10.5194/gmd-16-35-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-35-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
03 Jan 2023
Model description paper |
| 03 Jan 2023
| 03 Jan 2023
Prediction of algal blooms via data-driven machine learning models: an evaluation using data from a well-monitored mesotrophic lake
Erken Laboratory and Limnology Department, Uppsala University,
Uppsala, Sweden
Environment and Climate Change Canada, Canada Centre for Inland
Waters, Burlington, L7R 4A6 ON, Canada
Donald C. Pierson
Erken Laboratory and Limnology Department, Uppsala University,
Uppsala, Sweden
Jorrit P. Mesman
Erken Laboratory and Limnology Department, Uppsala University,
Uppsala, Sweden
Département F.-A. Forel des sciences de l'environnement et de
l'eau, Université de Genève, Geneva, Switzerland
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Cited
16 citations as recorded by crossref.
- Development and prediction of a robust multivariate trophic state index for the classification of lentic water bodies A. Suman et al. 10.1016/j.rineng.2023.101586
- Enhancing multi-step-ahead algal bloom forecasts in river ecosystems by a hybrid recursive deep learning model H. Xu et al. 10.2166/nh.2025.160
- Predicting Harmful Algal Blooms Using Explainable Deep Learning Models: A Comparative Study B. Demiray et al. 10.3390/w17050676
- A framework for developing a real-time lake phytoplankton forecasting system to support water quality management in the face of global change C. Carey et al. 10.1007/s13280-024-02076-7
- Projected response of algal blooms in global lakes to future climatic and land use changes: Machine learning approaches J. Ma et al. 10.1016/j.watres.2024.122889
- HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network T. Nguyen et al. 10.1016/j.ecoinf.2025.102994
- The role of industry 4.0 enabling technologies for predicting, and managing of algal blooms: Bridging gaps and unlocking potential A. Sheik et al. 10.1016/j.marpolbul.2024.117493
- Characterization and Modeling of Harmful Algal Blooms: A Review W. Astuti & R. Govindaraju 10.1061/JHEND8.HYENG-14108
- A comparative analysis of data-driven modeling approaches to forecast cyanobacteria algal blooms in eutrophic lake discharge canals H. Nguyen et al. 10.1016/j.jenvman.2025.124834
- Application of Optical Remote Sensing in Harmful Algal Blooms in Lakes: A Review S. Wang & B. Qin 10.3390/rs17081381
- Application of an integrated catchment-lake model approach for simulating effects of climate change on lake inputs and biogeochemistry I. Jiménez-Navarro et al. 10.1016/j.scitotenv.2023.163946
- Long-term prediction of algal chlorophyll based on empirical models and the machine learning approach in relation to trophic variation in Juam Reservoir, Korea S. Jin et al. 10.1016/j.heliyon.2024.e31643
- Are more data always better? – Machine learning forecasting of algae based on long-term observations D. Atton Beckmann et al. 10.1016/j.jenvman.2024.123478
- Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie O. Mermer & I. Demir 10.3390/app15094824
- Recent advances in algal bloom detection and prediction technology using machine learning J. Park et al. 10.1016/j.scitotenv.2024.173546
- Machine learning-based design and monitoring of algae blooms: Recent trends and future perspectives – A short review A. Sheik et al. 10.1080/10643389.2023.2252313
16 citations as recorded by crossref.
- Development and prediction of a robust multivariate trophic state index for the classification of lentic water bodies A. Suman et al. 10.1016/j.rineng.2023.101586
- Enhancing multi-step-ahead algal bloom forecasts in river ecosystems by a hybrid recursive deep learning model H. Xu et al. 10.2166/nh.2025.160
- Predicting Harmful Algal Blooms Using Explainable Deep Learning Models: A Comparative Study B. Demiray et al. 10.3390/w17050676
- A framework for developing a real-time lake phytoplankton forecasting system to support water quality management in the face of global change C. Carey et al. 10.1007/s13280-024-02076-7
- Projected response of algal blooms in global lakes to future climatic and land use changes: Machine learning approaches J. Ma et al. 10.1016/j.watres.2024.122889
- HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network T. Nguyen et al. 10.1016/j.ecoinf.2025.102994
- The role of industry 4.0 enabling technologies for predicting, and managing of algal blooms: Bridging gaps and unlocking potential A. Sheik et al. 10.1016/j.marpolbul.2024.117493
- Characterization and Modeling of Harmful Algal Blooms: A Review W. Astuti & R. Govindaraju 10.1061/JHEND8.HYENG-14108
- A comparative analysis of data-driven modeling approaches to forecast cyanobacteria algal blooms in eutrophic lake discharge canals H. Nguyen et al. 10.1016/j.jenvman.2025.124834
- Application of Optical Remote Sensing in Harmful Algal Blooms in Lakes: A Review S. Wang & B. Qin 10.3390/rs17081381
- Application of an integrated catchment-lake model approach for simulating effects of climate change on lake inputs and biogeochemistry I. Jiménez-Navarro et al. 10.1016/j.scitotenv.2023.163946
- Long-term prediction of algal chlorophyll based on empirical models and the machine learning approach in relation to trophic variation in Juam Reservoir, Korea S. Jin et al. 10.1016/j.heliyon.2024.e31643
- Are more data always better? – Machine learning forecasting of algae based on long-term observations D. Atton Beckmann et al. 10.1016/j.jenvman.2024.123478
- Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie O. Mermer & I. Demir 10.3390/app15094824
- Recent advances in algal bloom detection and prediction technology using machine learning J. Park et al. 10.1016/j.scitotenv.2024.173546
- Machine learning-based design and monitoring of algae blooms: Recent trends and future perspectives – A short review A. Sheik et al. 10.1080/10643389.2023.2252313
Latest update: 09 May 2025
Short summary
The risks brought by the proliferation of algal blooms motivate the improvement of bloom forecasting tools, but algal blooms are complexly controlled and difficult to predict. Given rapid growth of monitoring data and advances in computation, machine learning offers an alternative prediction methodology. This study tested various machine learning workflows in a dimictic mesotrophic lake and gave promising predictions of the seasonal variations and the timing of algal blooms.
The risks brought by the proliferation of algal blooms motivate the improvement of bloom...
