Joint identification of contaminant source based on the ensemble Kalman filter integrated with relation coefficient

Li, Jing; Kong, Jun; Wang, Jun; Xu, Teng; Pan, Mingjie; Chen, Wei‐Lun

doi:10.1016/j.jhydrol.2022.129057

Cited by 4 publications

(1 citation statement)

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“…These indicators might originate from various sources, such as ship ballast water discharges, industrial operations within the port areas and urban runoff associated with port activities. It is obvious that the only real monitoring system of water quality cannot cope with problems of pollutant source tracing [44] and the optimal allocation of emergency resources [45]. However, the parallel intelligent monitoring system of port water quality based on the ACP method can solve the above problems well.…”

Section: Discussionmentioning

confidence: 99%

Parallel Intelligent Monitoring System of Port Water Quality Based on the ACP Method

Guo,

Jiang,

Chu

et al. 2024

JMSE

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With the rapid development of port construction and the shipping industry, port water quality issues are of great concern. This is always a challenging task due to the frequent human activities and dynamic processes involved. A parallel intelligent water quality monitoring system is therefore proposed to ensure the effective monitoring and intelligent control of water pollutants. The real monitoring system and the artificial monitoring system of port water quality are established by applying artificial systems, computational experiments and parallel execution (ACP method). Both systems interact with each other and execute in parallel. The artificial monitoring system simulates complex scenarios, while the real monitoring system feeds the artificial monitoring system with actual monitoring data. By means of data-driven and model-driven approaches, the two systems can compute, observe and evaluate to control, manage and train models. Through the continuous optimization between the two systems, the efficiency and accuracy of the water quality monitoring system could be improved. Technical support can be further provided for the planning of water quality monitoring sites, implementation of monitoring tasks, allocation of emergency resources, etc. As in-situ monitoring data are obtained, computational experiments and parallel executions could be conducted to achieve the ultimate goal of port water quality management.

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Section: Discussionmentioning

confidence: 99%