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
Viewed
Total article views: 3,069 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 04 Jan 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,139 | 849 | 81 | 3,069 | 42 | 46 |
- HTML: 2,139
- PDF: 849
- XML: 81
- Total: 3,069
- BibTeX: 42
- EndNote: 46
Total article views: 1,845 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 20 Jun 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,334 | 463 | 48 | 1,845 | 34 | 37 |
- HTML: 1,334
- PDF: 463
- XML: 48
- Total: 1,845
- BibTeX: 34
- EndNote: 37
Total article views: 1,224 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 04 Jan 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 805 | 386 | 33 | 1,224 | 8 | 9 |
- HTML: 805
- PDF: 386
- XML: 33
- Total: 1,224
- BibTeX: 8
- EndNote: 9
Viewed (geographical distribution)
Total article views: 3,069 (including HTML, PDF, and XML)
Thereof 2,812 with geography defined
and 257 with unknown origin.
Total article views: 1,845 (including HTML, PDF, and XML)
Thereof 1,728 with geography defined
and 117 with unknown origin.
Total article views: 1,224 (including HTML, PDF, and XML)
Thereof 1,084 with geography defined
and 140 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
15 citations as recorded by crossref.
- A quantity-distribution synthesized framework for risk assessment of algal blooms T. Zhou et al. 10.1016/j.jhydrol.2023.129869
- 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
- A CPSOCGSA-tuned neural processor for forecasting river water salinity: Euphrates river, Iraq Z. Khudhair et al. 10.1080/23311916.2022.2150121
- 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
- Integrating temporal decomposition and data-driven approaches for predicting coastal harmful algal blooms Z. Yan & N. Alamdari 10.1016/j.jenvman.2024.121463
- Predicting coastal harmful algal blooms using integrated data-driven analysis of environmental factors Z. Yan et al. 10.1016/j.scitotenv.2023.169253
- 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
- Skill of seasonal flow forecasts at catchment scale: an assessment across South Korea Y. Lee et al. 10.5194/hess-28-3261-2024
- 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
- 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
- Bayesian Network Structural Learning Using Adaptive Genetic Algorithm with Varying Population Size R. Rodrigues Mendes Ribeiro & C. Dias Maciel 10.3390/make5040090
- Predicting Imminent Cyanobacterial Blooms in Lakes Using Incomplete Timely Data C. Heggerud et al. 10.1029/2023WR035540
- A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management M. Drogkoula et al. 10.3390/app132212147
- 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
- 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
14 citations as recorded by crossref.
- A quantity-distribution synthesized framework for risk assessment of algal blooms T. Zhou et al. 10.1016/j.jhydrol.2023.129869
- 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
- A CPSOCGSA-tuned neural processor for forecasting river water salinity: Euphrates river, Iraq Z. Khudhair et al. 10.1080/23311916.2022.2150121
- 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
- Integrating temporal decomposition and data-driven approaches for predicting coastal harmful algal blooms Z. Yan & N. Alamdari 10.1016/j.jenvman.2024.121463
- Predicting coastal harmful algal blooms using integrated data-driven analysis of environmental factors Z. Yan et al. 10.1016/j.scitotenv.2023.169253
- 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
- Skill of seasonal flow forecasts at catchment scale: an assessment across South Korea Y. Lee et al. 10.5194/hess-28-3261-2024
- 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
- 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
- Bayesian Network Structural Learning Using Adaptive Genetic Algorithm with Varying Population Size R. Rodrigues Mendes Ribeiro & C. Dias Maciel 10.3390/make5040090
- Predicting Imminent Cyanobacterial Blooms in Lakes Using Incomplete Timely Data C. Heggerud et al. 10.1029/2023WR035540
- A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management M. Drogkoula et al. 10.3390/app132212147
- 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
Latest update: 13 Dec 2024
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...
