Journal of Natural Resources and Agricultural Ecosystems, Год журнала: 2023, Номер 1(2), С. 63 - 76

Опубликована: Янв. 1, 2023

Highlights Machine Learning (ML) models are identified, reviewed, and analyzed for HAB predictions. Data preprocessing is vital efficient ML model development. toxin production monitoring limited. Abstract. Harmful algal blooms (HABs) detrimental to livestock, humans, pets, the environment, global economy, which calls a robust approach their management. While process-based can inform practitioners about enabling conditions, they have inherent limitations in accurately predicting harmful blooms. To address these limitations, potentially leverage large volumes of IoT data aid near real-time evolved as tools understanding patterns relationships between water quality parameters expansion. This review describes currently used forecasting HABs freshwater ecosystems presents structures application related toxins. The revealed that regression trees, random forest, Artificial Neural Network (ANN), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) most frequently monitoring. shows models' prowess identifying significant variables influencing growth, drivers, multistep prediction. Hybrid also improve prediction algal-related through improved optimization techniques variable selection algorithms. often focus on biomass prediction, few studies apply limitation be associated with lack high-frequency datasets development, exploring this domain encouraged. serves guide policymakers researchers implement reveals potential decision support early Keywords: Cyanobacteria, Freshwater, blooms, learning, Water quality.

Язык: Английский