MARISENSE Documentation

📖 Overview

"A maritime system is not a single unified domain — it is a collection of independent subsystems whose interaction must be governed through aggregation, not structural merging."

MARISENSE is a fully coupled, AI-augmented framework that treats maritime system health as a continuously governed composite indicator — not a static property frozen at the completion of a planning cycle.

A maritime system operating under dynamic conditions is not in static equilibrium. It is a continuously evolving collection of subsystems: Vessel Traffic, Port Operations, Ocean Environment, Maritime Safety, and Coastal Infrastructure. MARISENSE quantifies each subsystem independently and aggregates them into the Maritime System Health Index (MSHI).

🏗️ 5-Subsystem + AI Architecture

Subsystem 01 — VTS (Vessel Traffic System)

Macroscopic traffic flow model with AI correction (ε_V ≤ 0.05). Real-time AIS data integration and vessel density tracking.

VTS_score = 1 - (V_demand / C_capacity) + ε_V(t)
q = k·v · Fundamental diagram

Subsystem 02 — POS (Port Operations System)

Queuing theory-based efficiency model. Berth utilization and crane productivity metrics.

POS_score = (Throughput_actual / Throughput_target) * (1 - T_wait/T_wait_max)
ρ = λ/(c·μ) · Traffic intensity

Subsystem 03 — OES (Ocean Environmental System)

Physical oceanography-based condition assessment. Wave, current, and wind scoring.

OES_score = min(1 - H_s/H_limit, 1 - u/u_limit, 1 - w/w_limit)
H_s = 4√∫∫E(f,θ)dfdθ

Subsystem 04 — MSS (Maritime Safety System)

IMO Formal Safety Assessment (FSA) methodology. Incident probability modeling with Poisson process.

MSS_score = 1 - (R_estimated / R_critical)
R = Σ P(I_k)·C(I_k)

Subsystem 05 — CIS (Coastal Infrastructure System)

Asset management-based condition rating (1-5 scale). Multi-attribute assessment.

CIS_score = 1 - (Condition_rating / Critical_rating)
H = Σ α_j·C_j

AISL — AI Enhancement Layer

Gradient boosting weight predictor. Mahalanobis anomaly detection (3σ). 48h MSHI forecast. AI is ONLY for optimization and anomaly detection — not subsystem merging.

Σwᵢ = 1.0, wᵢ ≥ 0
MAH_dist² = (x - μ)ᵀ·Σ⁻¹·(x - μ) < 3σ
MSHI = w_V·VTS + w_P·POS + w_O·OES + w_M·MSS + w_C·CIS

MSHI — Maritime System Health Index

Weighted composite of all five subsystems. Continuous real-time maritime safety certification with 24-48h forecast.

MSHI = w_V·VTS + w_P·POS + w_O·OES + w_M·MSS + w_C·CIS
Optimized: MSHI ≥ 0.85 | Stressed: 0.70 ≤ MSHI < 0.85
Mitigation: 0.55 ≤ MSHI < 0.70 | Critical: MSHI < 0.55

📐 Core Equations

⚙️ MSHI Governance Protocol

📦 Installation

pip install marisense-engine

# From source
git clone https://github.com/gitdeeper13/MARISENSE.git
cd MARISENSE
pip install -e .

# Quick test
python -c "from marisense import MarisenseAssessor; print('MARISENSE ready')"

🔧 API Reference

from marisense import MarisenseAssessor

# Initialize assessor
assessor = MarisenseAssessor()

# Run full MARISENSE pipeline
result = assessor.evaluate()

print(result.mshi_result.mshi)          # MSHI ∈ [0,1]
print(result.mshi_result.signal.value)  # OPTIMIZED | STRESSED_WARNING | MITIGATION | CRITICAL
print(result.subsystem_scores)        # {VTS,POS,OES,MSS,CIS} scores
print(result.ai_weights)              # AI-optimized weights Σ=1.0
print(result.anomaly_detected)        # Mahalanobis distance > 3σ

📊 Validation Summary

👤 Author

📝 Citation

"A maritime system is not a single unified domain — it is a collection of independent subsystems whose interaction must be governed through aggregation, not structural merging. MARISENSE treats each maritime subsystem as analytically separate, applying AI only as a bounded optimization layer for the final composite indicator." — MARISENSE v1.0.0