Modeling Algal Toxin Dynamics and Integrated Web Framework for Lakes

Özlem Baydaroğlu; Serhan Yeşilköy; Anchit Dave; Marc Linderman; Ibrahim Demir

doi:10.3390/toxins17070338

Back

Modeling Algal Toxin Dynamics and Integrated Web Framework for Lakes

Journal article

Open access

Peer reviewed

Modeling Algal Toxin Dynamics and Integrated Web Framework for Lakes

Özlem Baydaroğlu, Serhan Yeşilköy, Anchit Dave, Marc Linderman and Ibrahim Demir

Toxins, Vol.17(7), 338

07/03/2025

DOI: 10.3390/toxins17070338

PMCID: PMC12298289

PMID: 40711149

Files and links (1)

url

https://doi.org/10.3390/toxins17070338View

Published (Version of record) Open Access

Abstract

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.

harmful algae

microcystin

sparse identification of nonlinear dynamics (SINDy)

environmental health

public health

web framework

Details

Title: Subtitle: Modeling Algal Toxin Dynamics and Integrated Web Framework for Lakes
Creators: Özlem Baydaroğlu - NOAA Earth System Research Laboratory
Serhan Yeşilköy - Oak Ridge Associated Universities
Anchit Dave - University of Iowa
Marc Linderman - University of Iowa
Ibrahim Demir - Tulane University
Resource Type: Journal article
Publication Details: Toxins, Vol.17(7), 338
DOI: 10.3390/toxins17070338
PMID: 40711149
PMCID: PMC12298289
NLM abbreviation: Toxins (Basel)
ISSN: 2072-6651
eISSN: 2072-6651
Publisher: MDPI
Grant note: University of Iowa Healthy Lakes Initiative
The University of Iowa Healthy Lakes Initiative provided the funding for this study.
Language: English
Date published: 07/03/2025
Academic Unit: Electrical and Computer Engineering; Civil and Environmental Engineering; Injury Prevention Research Center; Geographical and Sustainability Sciences
Record Identifier: 9984845653902771

Metrics

6 Record Views