HabLSTM: A Nonstationary Feature Focusing LSTM for Spatiotemporal Prediction of Harmful Algal Bloom

Harmful algal bloom (HAB) has long been one of the most formidable environmental problems in the world. HAB is influenced by multifactors, and its dynamic is highly nonstationary, making its prediction challenging. The existing machine learning (ML)-based HAB prediction methods mainly use time-series data, which ignore the intrinsic relationship between spatial and temporal variations in HAB. To achieve more accurate HAB spatiotemporal prediction, a novel long short-term memory (LSTM)-based nonstationary focusing prediction model (HabLSTM) is proposed in this article. The HabLSTM network is constructed by stacking HabLSTM units consisting of the hidden states spatial differential block (HSSD) and the combined states temporal differential (CSTD) block. The HSSD block uses the gating mechanism and the difference in hidden states to generate differential features between adjacent frames and guides the network to learn short-term nonstationary features by controlling the feature update of the hidden state in the HabLSTM unit. The CSTD block uses the gating mechanism and the difference in combined states to generate the differential features of the current input sequence and guides the network to learn long-term nonstationary features by controlling the feature update of the memory state in the HabLSTM unit. These two differential features guide the HabLSTM network to focus on learning nonstationary spatiotemporal features and boost HAB spatiotemporal prediction accuracy. In addition, two new spatiotemporal datasets of HAB named as Taihu HAB A and Taihu HAB B are established using the year-A and year-B normalized difference vegetation index (NDVI) images collected by Himawari-8 satellite, respectively. The experimental results on the two HAB datasets and spatiotemporal predictive learning (ST-PL) benchmark dataset MovingMNIST++ validate the outstanding HAB prediction and nonstationary spatiotemporal features’ learning capability of HabLSTM. The source code is available at
https://github.com/lxq-jnu/HabLSTM
.