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Cyanobacterial harmful algal blooms (cyanoHABs) caused by cyanobacteria negatively affect humans via river water and aquatic life. Thus, reliable cyanobacteria predictions are essential for managing cyanoHABs. With recent advancements in computer technology and big data usage, artificial intelligence (AI) technologies have gained attention in various fields, such as water resources, weather and climate, and water quality. This study evaluated the applicability of deep-learning-based AI technology for predicting cyanobacteria. A convolutional neural network (CNN)–long short-term memory (LSTM) model, a deep-learning-based AI technology advantageous for predicting time-series data and cyanobacteria features, was built. Its results were analyzed and compared with those of the existing physical Environmental Fluid Dynamics Code (EFDC)–National Institute of Environment Research (NIER) model for cyanobacteria prediction. The CNN-LSTM model performed better, with an accuracy of 69%, which is an improvement over the previous EFDC-NIER model's accuracy of 45%. In particular, there was a dramatic improvement in the prediction accuracy for low cyanobacteria cell counts in Level 1, which increased from 39% to 87%. There also was an improvement in the prediction accuracy for Levels 2 and 3. The accuracy for Level 2 increased from increased from 56% to 69%, and the accuracy for Level 3 increased from 38% to 48%. However, there was a significant decrease in prediction accuracy for high cyanobacteria cell counts in Level 4, for which the measured data were very scarce; accuracy decreased from 49% to 16.7%. The CNN-LSTM model yielded better overall prediction performance than the EFDC-NIER model, demonstrating its applicability in cyanobacteria prediction. However, it has limitations of overfitting areas with inadequate data and not accurately predicting patterns that have not occurred in the past. To address this issue, we propose an approach the combines the advantages of physics-based models and AI-based deep learning models, creating a hybrid concept. [ABSTRACT FROM AUTHOR]

Harmful algal blooms (HABs) caused by harmful cyanobacteria adversely impact the water quality in aquatic ecosystems and burden socioecological systems that are based on water utilization. Currently, Korea uses the Environmental Fluid Dynamics Code-National Institute of Environmental Research (EFDC-NIER) model to predict algae conditions and respond to algal blooms through the HAB alert system. This study aimed to establish an additional deep learning model to effectively respond to algal blooms. The prediction model is based on a deep neural network (DNN), which is a type of artificial neural network widely used for HAB prediction. By applying the synthetic minority over-sampling technique (SMOTE) to resolve the imbalance in the data, the DNN model showed improved performance during validation for predicting the number of cyanobacteria cells. The R-squared increased from 0.7 to 0.78, MAE decreased from 0.7 to 0.6, and RMSE decreased from 0.9 to 0.7, indicating an enhancement in the model's performance. Furthermore, regarding the HAB alert levels, the R-squared increased from 0.18 to 0.79, MAE decreased from 0.2 to 0.1, and RMSE decreased from 0.3 to 0.2, indicating improved performance as well. According to the results, the constructed data-based model reasonably predicted algae conditions in the summer when algal bloom-induced damage occurs and accurately predicted the HAB alert levels for immediate decision-making. The main objective of this study was to develop a new technology for predicting and managing HABs in river environments, aiming for a sustainable future for the aquatic ecosystem. [ABSTRACT FROM AUTHOR]