Explainable deep learning identifies patterns and drivers of freshwater harmful algal blooms.

ABSTRACT: The escalating magnitude, frequency, and duration of harmful algal blooms (HABs) pose significant challenges to freshwater ecosystems worldwide. However, the mechanisms driving HABs remain poorly understood, in part due to the strong regional specificity of algal processes and the uneven data availability. These complexities make it difficult to generalize HAB dynamics and effectively predict their occurrence using traditional models. To address these challenges, we developed an explainable deep learning approach using long short-term memory (LSTM) models combined with explanation techniques that can capture complex patterns and provide explainable insights into key HAB drivers. We applied this approach for algal density modeling at 102 sites in China's lakes and reservoirs over three year

SUBMITTER: Chen S 

PROVIDER: S-EPMC11786749 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature