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

• Published in: Journal of circuits, systems, and computers Vol. 33; no. 11
• Main Authors: Zheng, Jinjin, Li, Ning, Ye, Song
• Format: Journal Article
• Language: English
• Published: Singapore World Scientific Publishing Company 30.07.2024; World Scientific Publishing Co. Pte., Ltd
• Subjects: Algorithms; Artificial neural networks; Computer simulation; Deep learning; Feature extraction; Machine learning; Marine pollution; Monitoring; Neural networks; Oil spills; Spatiotemporal data; Time lag; Workflow; knowledge discovery; Spatiotemporal deep learning; marine pollutant incidents; data mining
• ISSN: 0218-1266; 1793-6454
• DOI: 10.1142/S0218126624501950

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.