Neurodynamics-Driven Prediction Model for State Evolution of Coastal Water Quality

Coastal water quality prediction is the indispensable work to prevent the red tide and marine pollution accidents, which also provides the effective assistance to study ocean carbon sink. Due to the multiple inducing factors and their spatiotemporal coupling effects, the water quality prediction not only needs to be supported by big data, but also needs an effective model for prediction and analysis. However, most of the existing models frequently use timeline data from the same section or local collection point and cannot realize inversion and traceability of inducing factors. In this article, we consider these tough problems and propose an effective neurodynamics-driven prediction model for state evolution of coastal water quality (NDPM-CWQ). First, an event-driven deep belief network (EDBN) is designed and trained using the spatiotemporal data. Second, through the sensitivity analysis of the input variables in EDBN model, we rank influence degrees of spatiotemporal variables on the water quality and give the inversion and traceability of inducing factors. Third, the convergence of training EDBN is analyzed from the perspective of the stationary distribution and decision stability of Markov chain. Finally, the practical data-based experimental results show that the proposed NDPM-CWQ not only achieves better prediction performance, but also can quantitatively analyze the inversion and traceability of inducing factors.