14 citations as recorded by crossref.

1. A quantity-distribution synthesized framework for risk assessment of algal blooms T. Zhou et al. 10.1016/j.jhydrol.2023.129869
2. 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
3. A CPSOCGSA-tuned neural processor for forecasting river water salinity: Euphrates river, Iraq Z. Khudhair et al. 10.1080/23311916.2022.2150121
4. Forecasting freshwater cyanobacterial harmful algal blooms for Sentinel-3 satellite resolved U.S. lakes and reservoirs B. Schaeffer et al. 10.1016/j.jenvman.2023.119518
5. Integrating temporal decomposition and data-driven approaches for predicting coastal harmful algal blooms Z. Yan & N. Alamdari 10.1016/j.jenvman.2024.121463
6. Predicting coastal harmful algal blooms using integrated data-driven analysis of environmental factors Z. Yan et al. 10.1016/j.scitotenv.2023.169253
7. Nutrient reduction mitigated the expansion of cyanobacterial blooms caused by climate change in Lake Taihu according to Bayesian network models J. Deng et al. 10.1016/j.watres.2023.119946
8. Skill of seasonal flow forecasts at catchment scale: an assessment across South Korea Y. Lee et al. 10.5194/hess-28-3261-2024
9. Conditional probability table limit-based quantization for Bayesian networks: model quality, data fidelity and structure score R. Rodrigues Mendes Ribeiro et al. 10.1007/s10489-023-05153-8
10. Bayesian Network Structural Learning Using Adaptive Genetic Algorithm with Varying Population Size R. Rodrigues Mendes Ribeiro & C. Dias Maciel 10.3390/make5040090
11. Predicting Imminent Cyanobacterial Blooms in Lakes Using Incomplete Timely Data C. Heggerud et al. 10.1029/2023WR035540
12. A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management M. Drogkoula et al. 10.3390/app132212147
13. Process-based forecasts of lake water temperature and dissolved oxygen outperform null models, with variability over time and depth W. Woelmer et al. 10.1016/j.ecoinf.2024.102825
14. A systems approach to modelling phosphorus pollution risk in Scottish rivers using a spatial Bayesian Belief Network helps targeting effective mitigation measures M. Glendell et al. 10.3389/fenvs.2022.976933