About this Research Topic
Data-driven ocean environmental perception refers to the various data-driven methodologies used by researchers to study, understand, and explore the ocean for sustainable marine ecosystem, the blue ocean economy, and other ocean-related activities. Ocean environment perception techniques also pave the way for deep insight and full exploration of ocean resources, such as coastal and marine ecosystem protection, ocean vehicle operation and management, and ocean energy utilization.
However, it is hard for traditional data processing techniques to get comprehensive understanding of the ocean environmental information attributed to the complex characteristics of environmental information. The traditional data processing techniques are facing challenges such as:
• Unable to fully capture the non-linearity and intrinsic uncertain of complex ocean systems;
• Isolated systems are involved and make it difficult to integrate data from various sources;
• Overwhelming for handling enormous amounts of data, leading to performance issues and long processing times;
• Costly in scalability both in terms of hardware and operational expenses.
Advances in data-driven methodologies:
• Highly dynamic nonlinearity and uncertainty mapping capability in complex system evolution;
• Leveraging vast amounts of data collected from various sources effectively;
• Flexibility in handling various types of data, including structured, semi-structured, and unstructured data;
• Handling big data efficiently by making use of distributed computing and storage.
Data-driven technologies are a set of techniques dealing with complex systems, which can mine valuable potential relationships from measurement data through intelligent computational approaches such as machine learning, data mining, data fusion, reinforcement learning, etc. Data-driven applications on the aspect of Remote Sensing, Autonomous Underwater Vehicles (AUVs), Remotely Operated Vehicles (ROVs), Acoustic Sensing, Buoy Networks, Internet of Things (IoT) for the oceans and Ocean Digital Twin are becoming increasingly crucial for ocean environmental perception, leveraging vast amounts of data collected from various sources to monitor, analyze, and predict oceanic-atmospheric and marine ecological conditions. This helps us to identify and monitor environmental changes and better understand the complex relationships between different components of the ocean ecosystem. It also provides essential ways to explore how people understand, value or engage with marine environment and the Blue Economy, such as renewable wind-wave energy, marine ranching industry for sustainable food energy, etc.
The focus of this research topic is dedicated to recent advances in data-driven technologies implemented for the field of ocean environment perception and their applications for the blue ocean economy, sustainable marine ecosystem and other ocean-related activities. This topic also aims to extend the future implications of data-driven techniques in the field of ocean research.
It calls for original and novel research related to ocean environmental perception based on data-driven techniques. Potential topics of interest include but are not limited to the following:
• Monitor, Analysis, and Prediction of Oceanic Conditions (ML and AI algorithms)
• Ocean Monitoring system, Buoy Networks, Satellite Remote Sensing, and Acoustic Sensing Applications
• Big Data, Internet of Things (IoT), Digital Twins for ocean and Smart Ocean
• Ocean Environmental Data Processing and Information Fusion Methods
• Data-Driven Control, Operation and Management of Ocean Vehicles (AUV, ROV, USV, et al.) for Ocean Environment perception and marine ecosystem monitoring
• Coastal and Marine Ecosystem Evaluation, Protection, and Management Based on Data-Driven Techniques
• Data-Driven Assessment and Effect Analysis of Climate Change and Human Activities on Oceans
• Global Marine Governance and Ocean Management in the Principle of Blue Economy, Sustainable Development Goals (SDGs) and UN Ocean Decade
• Utilization, Management and Governance of Renewable Ocean Energies (Tidal Currents, Waves, Wind, Thermal and Salinity Gradients, etc.) based on Data-Driven Ocean Environment Perception
• Management of Marine Infrastructure and Maritime Shipping Based on Ocean Environment Perception
However, it is hard for traditional data processing techniques to get comprehensive understanding of the ocean environmental information attributed to the complex characteristics of environmental information. The traditional data processing techniques are facing challenges such as:
• Unable to fully capture the non-linearity and intrinsic uncertain of complex ocean systems;
• Isolated systems are involved and make it difficult to integrate data from various sources;
• Overwhelming for handling enormous amounts of data, leading to performance issues and long processing times;
• Costly in scalability both in terms of hardware and operational expenses.
Advances in data-driven methodologies:
• Highly dynamic nonlinearity and uncertainty mapping capability in complex system evolution;
• Leveraging vast amounts of data collected from various sources effectively;
• Flexibility in handling various types of data, including structured, semi-structured, and unstructured data;
• Handling big data efficiently by making use of distributed computing and storage.
Data-driven technologies are a set of techniques dealing with complex systems, which can mine valuable potential relationships from measurement data through intelligent computational approaches such as machine learning, data mining, data fusion, reinforcement learning, etc. Data-driven applications on the aspect of Remote Sensing, Autonomous Underwater Vehicles (AUVs), Remotely Operated Vehicles (ROVs), Acoustic Sensing, Buoy Networks, Internet of Things (IoT) for the oceans and Ocean Digital Twin are becoming increasingly crucial for ocean environmental perception, leveraging vast amounts of data collected from various sources to monitor, analyze, and predict oceanic-atmospheric and marine ecological conditions. This helps us to identify and monitor environmental changes and better understand the complex relationships between different components of the ocean ecosystem. It also provides essential ways to explore how people understand, value or engage with marine environment and the Blue Economy, such as renewable wind-wave energy, marine ranching industry for sustainable food energy, etc.
The focus of this research topic is dedicated to recent advances in data-driven technologies implemented for the field of ocean environment perception and their applications for the blue ocean economy, sustainable marine ecosystem and other ocean-related activities. This topic also aims to extend the future implications of data-driven techniques in the field of ocean research.
It calls for original and novel research related to ocean environmental perception based on data-driven techniques. Potential topics of interest include but are not limited to the following:
• Monitor, Analysis, and Prediction of Oceanic Conditions (ML and AI algorithms)
• Ocean Monitoring system, Buoy Networks, Satellite Remote Sensing, and Acoustic Sensing Applications
• Big Data, Internet of Things (IoT), Digital Twins for ocean and Smart Ocean
• Ocean Environmental Data Processing and Information Fusion Methods
• Data-Driven Control, Operation and Management of Ocean Vehicles (AUV, ROV, USV, et al.) for Ocean Environment perception and marine ecosystem monitoring
• Coastal and Marine Ecosystem Evaluation, Protection, and Management Based on Data-Driven Techniques
• Data-Driven Assessment and Effect Analysis of Climate Change and Human Activities on Oceans
• Global Marine Governance and Ocean Management in the Principle of Blue Economy, Sustainable Development Goals (SDGs) and UN Ocean Decade
• Utilization, Management and Governance of Renewable Ocean Energies (Tidal Currents, Waves, Wind, Thermal and Salinity Gradients, etc.) based on Data-Driven Ocean Environment Perception
• Management of Marine Infrastructure and Maritime Shipping Based on Ocean Environment Perception
Keywords: ocean environment perception, machine learning, data mining, Data-Driven Techniques, Artificial Intelligence, Big Data, Smart Ocean
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