Harmful algal bloom prediction using empirical dynamic modeling

Abstract

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 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. From this point of view, this present study utilizes empirical dynamic modeling (EDM) to predict chlorophyll-a concentration of Lake Erie. This method is characterized by its nonlinearity and nonparametric nature. EDM has a key advantage in that it overcomes the limitations of traditional statistical modeling by utilizing data-driven attractor reconstruction. Chlorophyll-a is a critical parameter in the prediction of HAB events. Lake Erie is an inland water body that experiences frequent HAB phenomena due to its location. The EDM demonstrated exceptional performance, and these findings imply that the EDM model can effectively capture the underlying dynamics of chlorophyll-a changes.