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MeghanaK25/
ais-isolation-forest

Name: ais-isolation-forest
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unsupervised-learning

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🚢 AIS Maritime Anomaly Detection Models

A comprehensive suite of unsupervised machine learning models for detecting anomalous vessel behavior in AIS (Automatic Identification System) data, specifically designed for oil spill detection and maritime safety applications.

🎯 Model Overview

This repository contains 5 trained unsupervised anomaly detection models optimized for maritime AIS data analysis:

Model	Type	Accuracy	Anomaly Rate	Best For	Size
IsolationForest 🌟	Ensemble	100% contamination match	10.0%	Recommended - Best overall	1.1MB
LocalOutlierFactor	Density-based	100% contamination match	10.0%	Local anomaly detection	104MB
OneClassSVM	SVM-based	100% contamination match	10.0%	Non-linear patterns	3.9MB
EllipticEnvelope	Statistical	100% contamination match	10.0%	Gaussian distributed data	3.6MB
DBSCAN	Clustering	N/A (parameter-free)	2.1%	Cluster-based anomalies	39MB

🛢️ Oil Spill Detection Pipeline

These models are designed as Step 2A in a comprehensive oil spill detection system:

AIS Stream → Anomaly Detection → Sentinel-1 SAR Analysis → Oil Spill Alert

Key Anomaly Patterns Detected:

Stationary vessels during potential oil transfer operations
Unusual speed patterns (too fast/slow for vessel type)
Deep-draught vessels in unexpected locations
Course/heading inconsistencies indicating suspicious navigation
Loitering behavior in sensitive maritime areas

📊 Performance Metrics

IsolationForest (Recommended Model):

Contamination Accuracy: 100.00%
Score Separation: 0.154 (excellent discrimination)
Silhouette Score: 0.415 (good clustering quality)
Statistical Significance: 6/6 features significant
Processing Speed: ~30 seconds for 358K records

Feature Discrimination Power:

Speed (SOG): 51.6% difference between normal/anomalous
Draught: 31.2% difference (deep vessels suspicious)
Width: 9.5% difference
Course Difference: High effect size (0.927)

🚀 Quick Start

Installation

pip install numpy pandas scikit-learn joblib

Usage Example

python

import joblib
import numpy as np

# Load the recommended model
model_data = joblib.load('isolationforest_model.joblib')
model = model_data['model']
scaler = model_data['scaler']

# Prepare your AIS data with these features:
# ['sog', 'cog', 'heading', 'width', 'length', 'draught',
#  'navigationalstatus_encoded', 'shiptype_encoded', 
#  'speed_category', 'size_category', 'course_diff', 'aspect_ratio']

# Make predictions
features_scaled = scaler.transform(your_ais_features)
anomaly_scores = model.decision_function(features_scaled)
predictions = model.predict(features_scaled)

# Anomalies have prediction = -1, anomaly_score < 0
anomalous_vessels = features_scaled[predictions == -1]

🌍 Environmental Impact

This system contributes to:

Marine pollution prevention through early oil spill detection
Maritime safety via suspicious vessel identification
Environmental protection of sensitive marine areas
Regulatory compliance for maritime authorities

📁 Model Files

Available in both formats for maximum compatibility:

Pickle Format (.pkl) - Recommended for Hugging Face:

isolationforest_model.pkl (1.1MB) ⭐
localoutlierfactor_model.pkl (104MB)
oneclasssvm_model.pkl (3.9MB)
ellipticenvelope_model.pkl (3.6MB)
dbscan_model.pkl (39MB)

Joblib Format (.joblib) - For sklearn compatibility:

All models also available as .joblib files

Features

Data Processing

Handles missing values in AIS data
Encodes categorical variables (navigational status, ship type)
Creates derived features:
- Speed categories (stationary, slow, normal, fast)
- Vessel size categories
- Course difference (COG vs Heading)
- Aspect ratio (length/width)

Anomaly Detection

Uses Isolation Forest algorithm (unsupervised learning)
Configurable contamination parameter (default: 10% expected anomalies)
Provides anomaly scores and binary predictions

Analysis & Visualization

Statistical comparison between normal and anomalous vessels
Ship type analysis
Multiple visualization plots:
- Anomaly score distributions
- Speed vs vessel length scatter plots
- Ship type anomaly rates
- Course vs heading patterns
- Vessel dimensions analysis

Installation

Make sure you have Python 3.7+ installed
Install required packages:

bash

cd /Users/lakshmikotaru/Documents/ais_isolation_forest
pip install -r requirements.txt

Usage

Training the Model

Run the main script to train the Isolation Forest model:

python ais_anomaly_detection.py

This will:

Load the AIS data from /Users/lakshmikotaru/Downloads/ais_data.csv
Preprocess the data and create features
Train the Isolation Forest model
Generate analysis and visualizations
Save the trained model and results

Using the Trained Model

Use the prediction script to detect anomalies in new data:

bash

# Use with new data file
python predict_anomalies.py path/to/new_ais_data.csv

# Or run without arguments to use original data as example
python predict_anomalies.py

Model Configuration

You can adjust the model parameters in ais_anomaly_detection.py:

python

detector = AISAnomalyDetector(
    contamination=0.1,    # Expected fraction of anomalies (10%)
    random_state=42       # For reproducible results
)

Data Format

The AIS data should be a CSV file with the following columns:

mmsi: Maritime Mobile Service Identity
navigationalstatus: Current navigation status
sog: Speed Over Ground (knots)
cog: Course Over Ground (degrees)
heading: Vessel heading (degrees)
shiptype: Type of vessel
width: Vessel width (meters)
length: Vessel length (meters)
draught: Vessel draught (meters)

Output Files

`ais_isolation_forest_model.joblib`

The trained model file that can be loaded for future predictions.

`detected_anomalies.csv`

Detailed information about all detected anomalies, including:

Original vessel data
Anomaly scores
Binary anomaly flags

`anomaly_analysis_plots.png`

Comprehensive visualization showing:

Anomaly score distributions
Feature comparisons between normal and anomalous vessels
Ship type analysis
Various scatter plots and distributions

Interpretation

Anomaly Scores

Lower (more negative) scores indicate higher anomaly likelihood
Scores are relative to the training data distribution

Common Anomaly Types

The model may detect:

Vessels with unusual speed patterns
Ships with inconsistent course/heading relationships
Vessels with atypical dimensions for their type
Unusual combinations of vessel characteristics

Example Output

============================================================
AIS DATA ANOMALY DETECTION USING ISOLATION FOREST
============================================================
Loading data from /Users/lakshmikotaru/Downloads/ais_data.csv...
Loaded 358351 records with 9 columns

Training Isolation Forest model...
Model training completed!
Number of anomalies detected: 35835 out of 358351 samples
Anomaly rate: 10.00%

ANOMALY ANALYSIS SUMMARY
============================================================
Normal samples: 322516 (90.0%)
Anomalous samples: 35835 (10.0%)

Customization

Adding New Features

To add new derived features, modify the preprocess_data method in the AISAnomalyDetector class.

Changing Model Parameters

Adjust the IsolationForest parameters in the __init__ method:

n_estimators: Number of trees in the forest
contamination: Expected proportion of anomalies
max_samples: Number of samples to draw for each tree

Visualization

Modify the visualize_results method to add new plots or change existing ones.

Notes

The model is unsupervised, so it learns patterns without labeled anomalies
Results should be validated by domain experts
The contamination parameter significantly affects the number of detected anomalies
Missing values are handled automatically during preprocessing

Troubleshooting

Import errors: Make sure all requirements are installed
File not found: Check that the AIS data file path is correct
Memory issues: For very large datasets, consider processing in chunks
Plotting issues: Ensure matplotlib backend is properly configured

Contact

Generated for AIS Anomaly Detection Project - 2025-01-11

MeghanaK25/ais-isolation-forest

🚢 AIS Maritime Anomaly Detection Models

🎯 Model Overview

🛢️ Oil Spill Detection Pipeline

Key Anomaly Patterns Detected:

📊 Performance Metrics

IsolationForest (Recommended Model):

Feature Discrimination Power:

🚀 Quick Start

Installation

Usage Example

🌍 Environmental Impact

📁 Model Files

Pickle Format (.pkl) - Recommended for Hugging Face:

Joblib Format (.joblib) - For sklearn compatibility:

Features

Data Processing

Anomaly Detection

Analysis & Visualization

Installation

Usage

Training the Model

Using the Trained Model

Model Configuration

Data Format

Output Files

ais_isolation_forest_model.joblib

detected_anomalies.csv

anomaly_analysis_plots.png

Interpretation

Anomaly Scores

Common Anomaly Types

Example Output

Customization

Adding New Features

Changing Model Parameters

Visualization

Notes

Troubleshooting

Contact

MeghanaK25/
ais-isolation-forest

`ais_isolation_forest_model.joblib`

`detected_anomalies.csv`

`anomaly_analysis_plots.png`