OilSpill

Advanced Marine Environmental Protection

Revolutionary oil spill trajectory prediction using Liquid Time-Constant Networks and Multi-Agent Systems. Transforming emergency response with real-time adaptability and autonomous coordination.

System Demonstration 1
Oil spill trajectory prediction in action
System Demonstration 2
Multi-agent coordination showcase

Revolutionary Technology

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Liquid Time-Constant Networks

Advanced neural architecture that captures continuous-time dynamics of oil weathering processes and environmental forcing factors with unprecedented accuracy.

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Multi-Agent Coordination

Distributed intelligence system using MOOS-IvP framework enabling autonomous decision-making and fault-tolerant operations in dynamic marine environments.

Real-Time Adaptation

Continuous learning and adaptation to changing oceanographic conditions, weather patterns, and spill characteristics for optimal response effectiveness.

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Superior Performance

Validated superior performance over traditional LSTM methods across spatial accuracy, temporal consistency, area conservation, and physical realism metrics.

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Deepwater Horizon Validated

Comprehensive testing using historical oil spill data demonstrates practical viability across diverse environmental scenarios and operational conditions.

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Scalable Architecture

Three optimized solver variants (RK4, Explicit, Euler) tailored for different operational requirements from strategic planning to rapid assessment.

Technical Excellence

3x

Solver Variants
RK4 • Explicit • Euler

Real-Time

Continuous Adaptation
to Marine Conditions

Multi-Agent

Distributed Intelligence
MOOS-IvP Framework

Superior

Predictive Performance
vs Traditional LSTM

Advanced Methodology

Integrated Framework

Our comprehensive framework integrates liquid time-constant networks with multiagent systems to address fundamental limitations of existing oil spill prediction approaches while providing enhanced adaptability and real-time responsiveness.

Optimized Performance

Systematic evaluation of numerical solvers identifies optimal deployment strategies:

  • OilSpill-RK4: Maximum accuracy for strategic planning
  • OilSpill-Explicit: Exceptional stability for resource allocation
  • OilSpill-Euler: Computational efficiency for rapid assessment

System Architecture

Distributed sensing networks
Emergent formation control
Dynamic task allocation
Fault-tolerant operations

Contact the Research Team

Swarm & AI Lab
The Leon H. Charney School of Marine Sciences

Location: Room 276, Multi Purpose Bldg

University: University of Haifa

Address: 199 Abba Khoushy Ave.
Mount Carmel, Haifa, Israel

POB: 3338

Zip Code: 3103301

Phone: +972-4-8288790

Email: marine-info@univ.haifa.ac.il

Collaborate with Us

Interested in our oil spill prediction research? We welcome collaboration opportunities, academic partnerships, and discussions about implementing our technology for marine environmental protection.

University of Haifa • Leon H. Charney School of Marine Sciences