Modeling Algal Toxin Dynamics and Integrated Web Framework for Lakes
Harmful algal blooms (HABs) are one of the major environmental concerns, as they have various negative effects on public and environmental health, recreational services, and economics. HAB modeling is challenging due to inconsistent and insufficient data, as well as the nonlinear nature of algae for...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Toxins |
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| Online Access: | https://www.mdpi.com/2072-6651/17/7/338 |
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| author | Özlem Baydaroğlu Serhan Yeşilköy Anchit Dave Marc Linderman Ibrahim Demir |
| author_facet | Özlem Baydaroğlu Serhan Yeşilköy Anchit Dave Marc Linderman Ibrahim Demir |
| author_sort | Özlem Baydaroğlu |
| collection | DOAJ |
| description | Harmful algal blooms (HABs) are one of the major environmental concerns, as they have various negative effects on public and environmental health, recreational services, and economics. HAB modeling is challenging due to inconsistent and insufficient data, as well as the nonlinear nature of algae formation data. However, it is crucial for attaining sustainable development goals related to clean water and sanitation. From this point of view, we employed the sparse identification nonlinear dynamics (SINDy) technique to model microcystin, an algal toxin, utilizing dissolved oxygen as a water quality metric and evaporation as a meteorological parameter. SINDy is a novel approach that combines a sparse regression and machine learning method to reconstruct the analytical representation of a dynamical system. The model results indicate that MAPE values of approximately 2% were achieved in three out of four lakes, while the MAPE value of the remaining lake is 11%. Moreover, a model-driven and web-based interactive tool was created to develop environmental education, raise public awareness on HAB events, and produce more effective solutions to HAB problems through what-if scenarios. This interactive and user-friendly web platform allows tracking the status of HABs in lakes and observing the impact of specific parameters on harmful algae formation. |
| format | Article |
| id | doaj-art-e8d30bcb820e4a5fbebf665ee27d5593 |
| institution | Kabale University |
| issn | 2072-6651 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Toxins |
| spelling | doaj-art-e8d30bcb820e4a5fbebf665ee27d55932025-08-20T03:32:28ZengMDPI AGToxins2072-66512025-07-0117733810.3390/toxins17070338Modeling Algal Toxin Dynamics and Integrated Web Framework for LakesÖzlem Baydaroğlu0Serhan Yeşilköy1Anchit Dave2Marc Linderman3Ibrahim Demir4NOAA, Global Systems Laboratory, Boulder, CO 80305, USAİstanbul Provincial Directorate of Agriculture and Forestry, Ministry of Agriculture and Forestry, İstanbul 34724, TürkiyeIIHR–Hydroscience & Engineering, University of Iowa, Iowa City, IA 52242, USADepartment of Geographical and Sustainability Sciences, University of Iowa, Iowa City, IA 52245, USARiver-Coastal Science and Engineering, Tulane University, New Orleans, LA 70118, USAHarmful algal blooms (HABs) are one of the major environmental concerns, as they have various negative effects on public and environmental health, recreational services, and economics. HAB modeling is challenging due to inconsistent and insufficient data, as well as the nonlinear nature of algae formation data. However, it is crucial for attaining sustainable development goals related to clean water and sanitation. From this point of view, we employed the sparse identification nonlinear dynamics (SINDy) technique to model microcystin, an algal toxin, utilizing dissolved oxygen as a water quality metric and evaporation as a meteorological parameter. SINDy is a novel approach that combines a sparse regression and machine learning method to reconstruct the analytical representation of a dynamical system. The model results indicate that MAPE values of approximately 2% were achieved in three out of four lakes, while the MAPE value of the remaining lake is 11%. Moreover, a model-driven and web-based interactive tool was created to develop environmental education, raise public awareness on HAB events, and produce more effective solutions to HAB problems through what-if scenarios. This interactive and user-friendly web platform allows tracking the status of HABs in lakes and observing the impact of specific parameters on harmful algae formation.https://www.mdpi.com/2072-6651/17/7/338harmful algaemicrocystinsparse identification of nonlinear dynamics (SINDy)environmental healthpublic healthweb framework |
| spellingShingle | Özlem Baydaroğlu Serhan Yeşilköy Anchit Dave Marc Linderman Ibrahim Demir Modeling Algal Toxin Dynamics and Integrated Web Framework for Lakes Toxins harmful algae microcystin sparse identification of nonlinear dynamics (SINDy) environmental health public health web framework |
| title | Modeling Algal Toxin Dynamics and Integrated Web Framework for Lakes |
| title_full | Modeling Algal Toxin Dynamics and Integrated Web Framework for Lakes |
| title_fullStr | Modeling Algal Toxin Dynamics and Integrated Web Framework for Lakes |
| title_full_unstemmed | Modeling Algal Toxin Dynamics and Integrated Web Framework for Lakes |
| title_short | Modeling Algal Toxin Dynamics and Integrated Web Framework for Lakes |
| title_sort | modeling algal toxin dynamics and integrated web framework for lakes |
| topic | harmful algae microcystin sparse identification of nonlinear dynamics (SINDy) environmental health public health web framework |
| url | https://www.mdpi.com/2072-6651/17/7/338 |
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