Prediction of chlorophyll-a data based on triple-stage attention recurrent neural network

Marine Internet of Things (IOT) is the use of Internet technology to connect various sensing devices at sea, so as to integrate maritime information and realize the monitoring and systematic management of complex data at sea. The marine environment is complex and changeable, and marine disasters occur frequently, such as red tides. Due to the sudden and destructive nature of red tide, it plays a pivotal role to monitor the occurrence of the red tide for the marine IoT, where machine learning has been widely used to predict red tides. However, they were rarely able to catch the sudden change of chlorophyll-a, which has important practical significance for predicting the occurrence of red tide. In order to deal with the above problems, this paper proposes the triple-stage attention-based recurrent neural network, which can enhance the representation ability of input sequences, selectively capture dynamic spatial correlations between input multi-channel observations in the input sequence, meanwhile adaptively capturing dynamic temporal correlations between different time intervals in the input sequence. The results show that this method outperforms the state-of-art baseline methods here.