Abstract
Abstract This study employs advanced predictive modeling to forecast Harmful algal blooms (HABs) concentrations in South Korea, adopt Convolutional Long Short-Term Memory (ConvLSTM) models. The research integrates 3D universal kriging for spatial interpolation, leveraging latitude, longitude, and elevation data to create a comprehensive dataset. The ConvLSTM model, which integrates convolutional neural networks (CNNs) for spatial feature extraction and long short-term memory (LSTM) units for capturing temporal dependencies, was utilized on spatiotemporal data. The results demonstrate effectiveness in predicting HABs concentrations, offering valuable insights for environmental monitoring and management. This approach supports the development of sustainable strategies in line with environmental, social, and governance (ESG) principles, enhancing our ability to mitigate the adverse impacts of HABs on ecosystems and public health.
Published Version
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