A Spatiotemporal Deep Learning-Based Smart Discovery Approach for Marine Pollution Incidents from the Data-Driven Perspective

Abstract

Marine pollution incidents (MPI) are often a dynamic process of time and space interaction. Currently, the monitoring of MPI is basically realized by manual analysis from expert experience. Such working mode has an obvious time lag, and is not useful for timely disposal. As a result, intelligent algorithms that can make quick discovery for MPI from massive monitoring data remain a practical demand in this field. Considering that monitoring elements generally have multi-dimensional characteristics and spatiotemporal causal relationships, this work develops a spatiotemporal deep learning-based smart discovery approach for MPI from the data-driven perspective. In particular, a systematic preprocessing workflow is developed for the spatiotemporal monitoring data, which facilitates following feature extraction. Then, a spatiotemporal convolution neural network structure is developed to extract features from original spatiotemporal monitoring data. On this basis, the discovery results of MPI can be output via neural computing structures. Taking the polluting marine oil spill incident in the Bohai Sea in eastern China as a case study, this work carries out a simulation application and its result analysis. The obtained simulation results can reveal the proper performance of the proposal.