Harrmful Algal Bloom Prediction using Emprical Dynamic Modelling

Harmful Algal Blooms (HABs) can originate from a variety of reasons, including water pollution coming from agriculture, effluent from treatment plants, sewage system leaks, pH and light levels, and the consequences of climate change. In recent years, HAB events have become a serious environmental problem, paralleling population growth, agricultural development, increasing air temperatures, and declining precipitation. Hence, it is crucial to identify the mechanisms responsible for the formation of harmful algal blooms (HABs), accurately assess their short- and long-term impacts, and quantify their variations based on climate projections for developing accurate action plans and effectively managing resources. This present study utilizes empirical dynamic modeling (EDM) to predict chlorophyll-a (chl-a) concentration of Lake Erie. This method is characterized by its nonlinearity and nonparametric nature. EDM has a significant benefit in that it surpasses the constraints of conventional statistical modeling through the use of data-driven attractor reconstruction. Chl-a is a critical and commonly used parameter in the prediction of HAB events. Lake Erie is an inland water body that experiences frequent HAB phenomena as a result of its location. With a MAPE of 4.31%, an RMSE of 6.24, and a coefficient of determination of 0.98, the EDM showed exceptional performance. These findings suggest that the underlying dynamics of chl-a changes can be well captured by the EDM model.