# Modeling Algal Toxin Dynamics and Integrated Web Framework for Lakes

## TL;DR

This paper introduces a new model and web tool to predict and manage harmful algal blooms in lakes using data-driven methods.

## Contribution

A novel application of SINDy to model algal toxins and a web framework for public engagement and HAB mitigation.

## Key findings

- The SINDy model achieved MAPE values of about 2% in three lakes and 11% in one lake for predicting microcystin levels.
- A web-based interactive tool was developed to track HABs and simulate the impact of environmental parameters.

## 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.