Articles | Volume 26, issue 12
https://doi.org/10.5194/hess-26-3103-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-3103-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Jun 2022
Research article |
| 20 Jun 2022
| 20 Jun 2022
Seasonal forecasting of lake water quality and algal bloom risk using a continuous Gaussian Bayesian network
Leah A. Jackson-Blake
CORRESPONDING AUTHOR
Norwegian Institute for Water Research (NIVA), 0599 Oslo, Norway
François Clayer
Norwegian Institute for Water Research (NIVA), 0599 Oslo, Norway
Sigrid Haande
Norwegian Institute for Water Research (NIVA), 0599 Oslo, Norway
James E. Sample
Norwegian Institute for Water Research (NIVA), 0599 Oslo, Norway
S. Jannicke Moe
Norwegian Institute for Water Research (NIVA), 0599 Oslo, Norway
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Cited
30 citations as recorded by crossref.
- A CPSOCGSA-tuned neural processor for forecasting river water salinity: Euphrates river, Iraq Z. Khudhair et al.
- Multivariate Statistical Modeling of Seasonal River Water Quality Using Limited Hydrological and Climatic Data O. Mohamed & N. Hirayama
- Can causal discovery lead to a more robust prediction model for runoff signatures? H. Abbasizadeh et al.
- Modelling land use influence on polymer-specific microplastics abundance and transportation from terrestrial to aquatic environments M. Gunasekara et al.
- Predicting coastal harmful algal blooms using integrated data-driven analysis of environmental factors Z. Yan et al.
- Nutrient reduction mitigated the expansion of cyanobacterial blooms caused by climate change in Lake Taihu according to Bayesian network models J. Deng et al.
- Skill of seasonal flow forecasts at catchment scale: an assessment across South Korea Y. Lee et al.
- Conditional probability table limit-based quantization for Bayesian networks: model quality, data fidelity and structure score R. Rodrigues Mendes Ribeiro et al.
- Bayesian Network Structural Learning Using Adaptive Genetic Algorithm with Varying Population Size R. Rodrigues Mendes Ribeiro & C. Dias Maciel
- Predicting Oxythermal Stress Conditions for Coldwater Fish in a Northern Wisconsin Lake E. Blackford et al.
- A Comprehensive Review and Application of Bayesian Methods in Hydrological Modelling: Past, Present, and Future Directions K. Haddad
- A Review of Water Quality Forecasting Models for Freshwater Lentic Ecosystems J. García-Guerrero et al.
- Systematic review and topic classification of soft computing and machine learning in water resources management M. Drogkoula et al.
- From Monitoring Data to Management Decisions: Causal Network Analysis of Water Quality Dynamics Using CEcBaN S. Hilau et al.
- Process-based forecasts of lake water temperature and dissolved oxygen outperform null models, with variability over time and depth W. Woelmer et al.
- An explainable ensemble machine-learning framework for short-term forecasting of algal blooms across contrasting Lake systems W. Wang et al.
- A quantity-distribution synthesized framework for risk assessment of algal blooms T. Zhou et al.
- 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.
- Exploring the value of seasonal flow forecasts for drought management in South Korea Y. Lee et al.
- Predictive modelling of cyanobacterial blooms: evaluating climate scenarios and management strategies for drinking water reservoirs C. Acuña-Alonso et al.
- The need for advancing algal bloom forecasting using remote sensing and modeling: Progress and future directions C. Caballero et al.
- Forecasting freshwater cyanobacterial harmful algal blooms for Sentinel-3 satellite resolved U.S. lakes and reservoirs B. Schaeffer et al.
- Integrating temporal decomposition and data-driven approaches for predicting coastal harmful algal blooms Z. Yan & N. Alamdari
- Are more data always better? – Machine learning forecasting of algae based on long-term observations D. Atton Beckmann et al.
- Advances in forecasting of harmful algal blooms in freshwater ecosystems K. Campbell & R. Vinebrooke
- Predicting Imminent Cyanobacterial Blooms in Lakes Using Incomplete Timely Data C. Heggerud et al.
- Stressor-driven changes in freshwater biological indicators inform spatial management strategies using expert knowledge, observational data, and hierarchical models S. Emmons et al.
- Next-generation water quality monitoring: sensor-based deep learning prediction and calibration optimization in urban rivers Z. Wang et al.
- Adaptive Simplified Calculation of Algal Bloom Risk Index for Reservoir-Type Drinking Water Sources Based on Improved TOPSIS and Identification of Risk Areas S. Ji et al.
- A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management M. Drogkoula et al.
30 citations as recorded by crossref.
- A CPSOCGSA-tuned neural processor for forecasting river water salinity: Euphrates river, Iraq Z. Khudhair et al.
- Multivariate Statistical Modeling of Seasonal River Water Quality Using Limited Hydrological and Climatic Data O. Mohamed & N. Hirayama
- Can causal discovery lead to a more robust prediction model for runoff signatures? H. Abbasizadeh et al.
- Modelling land use influence on polymer-specific microplastics abundance and transportation from terrestrial to aquatic environments M. Gunasekara et al.
- Predicting coastal harmful algal blooms using integrated data-driven analysis of environmental factors Z. Yan et al.
- Nutrient reduction mitigated the expansion of cyanobacterial blooms caused by climate change in Lake Taihu according to Bayesian network models J. Deng et al.
- Skill of seasonal flow forecasts at catchment scale: an assessment across South Korea Y. Lee et al.
- Conditional probability table limit-based quantization for Bayesian networks: model quality, data fidelity and structure score R. Rodrigues Mendes Ribeiro et al.
- Bayesian Network Structural Learning Using Adaptive Genetic Algorithm with Varying Population Size R. Rodrigues Mendes Ribeiro & C. Dias Maciel
- Predicting Oxythermal Stress Conditions for Coldwater Fish in a Northern Wisconsin Lake E. Blackford et al.
- A Comprehensive Review and Application of Bayesian Methods in Hydrological Modelling: Past, Present, and Future Directions K. Haddad
- A Review of Water Quality Forecasting Models for Freshwater Lentic Ecosystems J. García-Guerrero et al.
- Systematic review and topic classification of soft computing and machine learning in water resources management M. Drogkoula et al.
- From Monitoring Data to Management Decisions: Causal Network Analysis of Water Quality Dynamics Using CEcBaN S. Hilau et al.
- Process-based forecasts of lake water temperature and dissolved oxygen outperform null models, with variability over time and depth W. Woelmer et al.
- An explainable ensemble machine-learning framework for short-term forecasting of algal blooms across contrasting Lake systems W. Wang et al.
- A quantity-distribution synthesized framework for risk assessment of algal blooms T. Zhou et al.
- 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.
- Exploring the value of seasonal flow forecasts for drought management in South Korea Y. Lee et al.
- Predictive modelling of cyanobacterial blooms: evaluating climate scenarios and management strategies for drinking water reservoirs C. Acuña-Alonso et al.
- The need for advancing algal bloom forecasting using remote sensing and modeling: Progress and future directions C. Caballero et al.
- Forecasting freshwater cyanobacterial harmful algal blooms for Sentinel-3 satellite resolved U.S. lakes and reservoirs B. Schaeffer et al.
- Integrating temporal decomposition and data-driven approaches for predicting coastal harmful algal blooms Z. Yan & N. Alamdari
- Are more data always better? – Machine learning forecasting of algae based on long-term observations D. Atton Beckmann et al.
- Advances in forecasting of harmful algal blooms in freshwater ecosystems K. Campbell & R. Vinebrooke
- Predicting Imminent Cyanobacterial Blooms in Lakes Using Incomplete Timely Data C. Heggerud et al.
- Stressor-driven changes in freshwater biological indicators inform spatial management strategies using expert knowledge, observational data, and hierarchical models S. Emmons et al.
- Next-generation water quality monitoring: sensor-based deep learning prediction and calibration optimization in urban rivers Z. Wang et al.
- Adaptive Simplified Calculation of Algal Bloom Risk Index for Reservoir-Type Drinking Water Sources Based on Improved TOPSIS and Identification of Risk Areas S. Ji et al.
- A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management M. Drogkoula et al.
Saved (final revised paper)
Latest update: 30 Apr 2026
Short summary
We develop a Gaussian Bayesian network (GBN) for seasonal forecasting of lake water quality and algal bloom risk in a nutrient-impacted lake in southern Norway. Bayesian networks are powerful tools for environmental modelling but are almost exclusively discrete. We demonstrate that a continuous GBN is a promising alternative approach. Predictive performance of the GBN was similar or improved compared to a discrete network, and it was substantially less time-consuming and subjective to develop.
We develop a Gaussian Bayesian network (GBN) for seasonal forecasting of lake water quality and...
