Temporal Prediction of Chlorophyll Concentration in Coastal Waters Based on Multi-characteristics Modeling of Spatio-temporal Evolution

‍ ‍Chlorophyll plays an important role in the study of marine ecosystem. However， chlorophyll concentration is affected by many coupling factors， so it is a difficult problem to predict chlorophyll concentration accurately. These factors include not only the prediction of the site’s own attributes， but also the range of other site attributes that can have an impact on the chlorophyll concentration of the prediction site. At present， the time series prediction of chlorophyll concentration is realized by only considering the self-factor of prediction point， and the effect of space factor on chlorophyll concentration is ignored. Moreover， the combination of flat factors lacks structure， and it is difficult to achieve reasonable feature expression. In this paper， a data-driven spatio-temporal aggregation model is proposed to predict chlorophyll concentration. In this model， autocorrelation time series prediction model and multi-view spatial fusion prediction model are dynamically combined， and the influence of sudden change of environmental factors is considered. This paper evaluates the models using 2017 data from the Bohai Sea Region， and the results are superior to several baseline methods.