OilGuard
Smart detection, swift recovery: AI-driven oil spill solutions for a cleaner, safer ocean environment.
About
The problem it solves # Overview of the Project: AI-Driven Solution for Oil Spill Detection and Cleanup - **Problem**: Oil spills in marine environments harm marine life, coastal habitats, and industries. Traditional methods of detection and cleanup are slow and heavily manual. - **Solution**: Our project uses AI technology to provide a faster, safer, and more efficient way to detect and clean up oil spills. ## Core Innovation - **AIS Data and Satellite Imagery**: The system combines AIS data with satellite images for effective monitoring of oil spills. - **Machine Learning Models**: These models predict anomalies in AIS data and use real-time satellite images to identify potential spills. - **Reduced False Alarms**: The system reduces false alarms, allowing for faster response and minimizing ecological damage. ## Autonomous Response - **ASV Deployment**: Autonomous Surface Vehicles (ASVs) with **booms, skimmers, and tankers** are deployed to collect and remove oil from the water. - **Autonomous Operation**: ASVs operate autonomously in hazardous or hard-to-reach areas, reducing the need for human involvement. ## Economic and Preventive - **Cost-Effective**: The technology reduces the need for manual deployment, lowering overall costs. - **Predictive Capabilities**: It provides early warnings to maritime authorities and oil companies about high-risk zones. - **Investments**: One-time investment and further maintenance. - **Real-Time Response**: The system quickly moves from detection to containment, minimizing response time. ## Target Audience and Impact - **Audience**: Governments, oil companies, and coastal authorities responsible for marine safety and environmental compliance. - **Impact**: Provides automated, continuous monitoring, simplifying oil spill detection and cleanup. This protects marine biodiversity and coastal economies, reducing environmental damage.
How it's made
Challenges we ran into # Challenges Faced and Solutions ## Data Inconsistency and Cleaning - **Problem**: Data inconsistency was a major issue, needing extensive cleaning. - **AIS Data Issues**: Missing or incorrect values in key fields like ‘Navigational Status’ and ‘ROT’ (Rate of Turn) affected anomaly detection. - **Preprocessing**: We developed scripts to clean and preprocess the data for our machine learning models. - **Steps Taken**: - **Identifying Outliers**: Pinpointed outliers to prevent skewed results. - **Handling Missing Values**: Addressed missing data through imputation or removal. - **Normalization**: Standardized the dataset to ensure consistency in training. ## ASV Prototype Development ### Vessel Stability - **Challenge**: The initial monohull design was unstable, especially in rough waters. - **Solution**: Switched to a catamaran hull design, which improved stability by balancing weight between two hulls, reducing the risk of capsizing in challenging conditions. ### Motor System and Control - **Problem**: A single motor caused maneuverability and control issues. - **Solution**: Adopted a dual-motor system, with one motor per hull, enhancing control and navigation precision. - **Relay Synchronization**: - **Challenge**: Synchronizing two relays for the motors was complex. - **Solution**: Simplified the design by using one relay to control both motors, reducing complexity and improving reliability. - **Outcome**: These solutions refined the ASV prototype, making it more effective for oil spill recovery operations.