# Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie
## 4 Conclusions  
This study presents a data-driven framework for identifying and quantifying key environmental drivers of Harmful Algal Blooms (HABs) in Lake Erie using multivariate regression models. Based on seven years of in situ water quality data, we developed predictive models for chlorophyll-a (Chl-a) and total suspended solids (TSS), achieving strong performance with R\({}^{2}\) values of 0.973 and 0.958, respectively.  
Our analysis reveals that PON, turbidity, and POC are influential in predicting bloom intensity. However, it is critical to differentiate between causal drivers, such as total phosphorus (TP), and diagnostic indicators, such as turbidity and PON. While TP can be managed through watershed interventions, diagnostic indicators provide real-time feedback that a bloom is underway and can support immediate operational responses. This dual understanding informs both strategic prevention and tactical mitigation.  
The proposed regression framework can support short-term forecasting (1-2 weeks) and can help guide timely decisions, such as issuing advisories, closing recreational areas, or increasing sampling efforts. Although the model is developed for the western basin of Lake Erie, the underlying methodology may be adapted for use in other nutrient-impacted freshwater systems.  
While this study provides a valuable framework, there are several opportunities to address in future work. The current models rely on linear relationships and region-specific data. Incorporating meteorological, hydrological, and land-use variables, as well as satellite-derived indices and real-time monitoring platforms, may enhance both spatial transferability and early detection capacity. Moreover, integrating machine learning methods--particularly hybrid models that combine regression with deep learning--can improve the representation of complex, nonlinear interactions and enable dynamic forecasting across diverse hydroclimatic regimes.  
In summary, this research offers a practical and interpretable model for HAB prediction, reinforces the importance of phosphorus reduction, and highlights actionable indicators for timely intervention. These insights provide a valuable decision-support tool for environmental managers, public health officials, and water policy makers tasked with mitigating the risks and impacts of HABs in freshwater ecosystems.  
O.M.: conceptualization, methodology, validation, data curation, visualization, formal analysis, and writing--original draft preparation; I.D.: writing--review and editing, validation, supervision, project administration, and funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data used in this study are available in the NOAA National Centers for Environmental Information (NCEI) data repository at [[https://doi.org/10.25921/11](https://doi.org/10.25921/11) da-3x54]([https://doi.org/10.25921/11](https://doi.org/10.25921/11) da-3x54) (accessed on 15 September 2023). Detailed descriptions of these data were also presented in [PERSON] et al. [17].  
O.M. would like to give special thanks to the Next Generation Internet Transatlantic Fellowship Program for their generous support and funding, which has been instrumental in the completion of this work.  
The authors declare no conflicts of interest.