Résumé
Eutrophication episodes are common in freshwater and coastal environments, causing significant damage to drinking water and aquaculture. Predictive models are efficient approaches for anticipating eutrophication or algal blooms because ecologists and environmentalists can estimate water pollution levels and take appropriate precautionary steps ahead of time. In aquatic ecosystems, chlorophyll-a (Chl-a) can be employed as a water quality indicator, revealing information on man-made physical, chemical, and biological changes variations or seasonal interventions. In the present study, a Seasonal AutoRegressive Integrated Moving Average (SARIMA) model was developed to forecast monthly Chl-a concentrations in the North Lagoon of Tunis, a Ramsar site, and one of the most important lagoons in Tunisia, using approximately three decades of historical data, starting from January 1989 to April 2018. SARIMA (2,0,2)(2,0,2) 12 was found to be the best-fitting model for Chl-a forecasting in the North Lagoon of Tunis. The resulting SARIMA model was validated with actual monthly Chl-a concentrations from our last observations. Furthermore, with only one input variable, the SARIMA model showed greater applicability as a eutrophication early warning system using actual past Chl-a data. Finally, the SARIMA model was utilized to anticipate Chl-a levels from May 2018 to December 2025 as an early warning system for ecosystem managers and decision-makers for next generations.