31 citations as recorded by crossref.

1. Development and prediction of a robust multivariate trophic state index for the classification of lentic water bodies A. Suman et al. https://doi.org/10.1016/j.rineng.2023.101586
2. Recent Advances of Artificial Intelligence in Aquatic Bioindicators and Ecological Assessment K. Hu et al. https://doi.org/10.1007/s11270-025-08907-x
3. A Critical Review of Cyanoblooms and Cyanotoxins: Risk Assessment on Human Health and Agriculture along with Mitigation Strategies Using Machine Learning Perspectives V. Singh et al. https://doi.org/10.1021/acs.chas.5c00045
4. 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. https://doi.org/10.1007/s13280-024-02076-7
5. Cyanobacterial concentrations cause significant welfare declines to recreational fisheries: Evidence from a bloom-prone urban lake J. Smith & C. Lamborn https://doi.org/10.1016/j.indic.2026.101280
6. Projected response of algal blooms in global lakes to future climatic and land use changes: Machine learning approaches J. Ma et al. https://doi.org/10.1016/j.watres.2024.122889
7. Physical and Biogeochemical Drivers for Forecasting Red Tides in Southwest Florida: A Regionally Integrated Machine Learning Framework M. Duus et al. https://doi.org/10.3390/environments13050239
8. Application of Optical Remote Sensing in Harmful Algal Blooms in Lakes: A Review S. Wang & B. Qin https://doi.org/10.3390/rs17081381
9. Enhancing riverine cyanobacterial bloom prediction: A hybrid deep learning approach combining wavelet decomposition, double-layer LSTM, ARIMA, and residual compensation B. Liu et al. https://doi.org/10.1016/j.envsoft.2025.106764
10. Early warning of harmful cyanobacteria blooms based on high frequency in situ monitoring and intelligible machine learning modelling: The case study of Lake Müggelsee (Germany) F. Recknagel et al. https://doi.org/10.1016/j.watres.2025.124514
11. Are more data always better? – Machine learning forecasting of algae based on long-term observations D. Atton Beckmann et al. https://doi.org/10.1016/j.jenvman.2024.123478
12. Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie O. Mermer & I. Demir https://doi.org/10.3390/app15094824
13. Paralytic shellfish poisoning risk assessment in the west coast of Canada C. Bi et al. https://doi.org/10.1016/j.jhazmat.2025.140459
14. LakeBeD-US: a benchmark dataset for lake water quality time series and vertical profiles B. McAfee et al. https://doi.org/10.5194/essd-17-3141-2025
15. Machine learning-based design and monitoring of algae blooms: Recent trends and future perspectives – A short review A. Sheik et al. https://doi.org/10.1080/10643389.2023.2252313
16. Effect of Data Gaps on Harmful Algal Bloom Prediction Models for Inland Lakes W. Astuti & R. Govindaraju https://doi.org/10.1061/JHYEFF.HEENG-6544
17. The synergy of artificial intelligence and algal systems for sustainable wastewater treatment and carbon sequestration P. Obidi & D. Bayless https://doi.org/10.1080/21622515.2025.2581330
18. Coupling SWAT+, GOTM-WET, and LSTM to predict daily DO in Mar Menor S. Asadi et al. https://doi.org/10.1016/j.rineng.2025.107907
19. Unlocking the algae toolbox: Cutting-edge tools for environmental and biotechnological solutions V. Bui et al. https://doi.org/10.1016/j.biotechadv.2025.108652
20. Enhancing multi-step-ahead algal bloom forecasts in river ecosystems by a hybrid recursive deep learning model H. Xu et al. https://doi.org/10.2166/nh.2025.160
21. Predicting Harmful Algal Blooms Using Explainable Deep Learning Models: A Comparative Study B. Demiray et al. https://doi.org/10.3390/w17050676
22. Satellite-Observed Acceleration in the Occurrence of Compound Marine Heatwave and Phytoplankton Bloom Events in the Global Coastal Ocean J. Ma & C. Wang https://doi.org/10.3390/rs18091322
23. HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network T. Nguyen et al. https://doi.org/10.1016/j.ecoinf.2025.102994
24. The role of industry 4.0 enabling technologies for predicting, and managing of algal blooms: Bridging gaps and unlocking potential A. Sheik et al. https://doi.org/10.1016/j.marpolbul.2024.117493
25. Characterization and Modeling of Harmful Algal Blooms: A Review W. Astuti & R. Govindaraju https://doi.org/10.1061/JHEND8.HYENG-14108
26. Remote sensing identification and model-based prediction of harmful algal blooms in inland waters: Current insights and future perspectives W. Wang et al. https://doi.org/10.1016/j.wroa.2025.100369
27. Generalizable deep learning forecasting of harmful algal blooms using transfer learning across river systems J. Park et al. https://doi.org/10.1016/j.ecoinf.2025.103481
28. A comparative analysis of data-driven modeling approaches to forecast cyanobacteria algal blooms in eutrophic lake discharge canals H. Nguyen et al. https://doi.org/10.1016/j.jenvman.2025.124834
29. Application of an integrated catchment-lake model approach for simulating effects of climate change on lake inputs and biogeochemistry I. Jiménez-Navarro et al. https://doi.org/10.1016/j.scitotenv.2023.163946
30. 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. https://doi.org/10.1016/j.heliyon.2024.e31643
31. Recent advances in algal bloom detection and prediction technology using machine learning J. Park et al. https://doi.org/10.1016/j.scitotenv.2024.173546