Maritime Systems Intelligence · MARITIME-INTEL-01 · Version 1.0.0 · May 2026

MARISENSE

Maritime Systems Intelligence Framework
Independent Subsystem Modeling with AI-Assisted Aggregation
Vessel Traffic · Port Operations · Ocean Environment · Maritime Safety · Coastal Infrastructure

"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: the Maritime System Health Index (MSHI)."

↗ View on GitHub 📦 PyPI Package 🔬 Zenodo DOI 🦊 GitLab Mirror

Real-Time Maritime Governance

Four-Level MSHI Signal Classification

Every maritime configuration evaluated by MARISENSE receives a continuous safety signal with full subsystem diagnostics.

MSHI ≥ 0.85

🟢 OPTIMIZED MARITIME OPERATIONS

All subsystems within design bounds. Normal operation active — continuous monitoring.

0.70 ≤ MSHI < 0.85

🟠 STRESSED SUBSYSTEM WARNING

One or more subsystems below threshold — activate anomaly detection and targeted response.

0.55 ≤ MSHI < 0.70

🟠 SYSTEMIC MITIGATION PHASE

Multiple subsystem degradation — apply cross-subsystem balancing measures.

MSHI < 0.55

🔴 CRITICAL MARITIME BREACH

Safety threshold breached. Immediate operational shutdown — emergency resource deployment.

Governance Architecture

Five Subsystems · One Composite Index · Continuous Assessment

Each subsystem remains structurally independent and analytically separate. AI provides weight optimization and anomaly detection only.

SUBSYSTEM 01

VTS — Vessel Traffic System

T_score = [1 - (V_demand/C_capacity)] + ε_V

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

q = k·v · Fundamental diagram

SUBSYSTEM 02

POS — Port Operations System

P_score = (Throughput_actual / Throughput_target) * (1 - T_wait/T_wait_max)

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

ρ = λ/(c·μ) · Traffic intensity

SUBSYSTEM 03

OES — Ocean Environmental System

E_score = min(1 - H_s/H_limit, 1 - u/u_limit, 1 - w/w_limit)

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

H_s = 4√∫∫E(f,θ)dfdθ

SUBSYSTEM 04

MSS — Maritime Safety System

S_score = 1 - (R_estimated / R_critical)

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

R = Σ P(I_k)·C(I_k)

SUBSYSTEM 05

CIS — Coastal Infrastructure System

I_score = 1 - (Condition_rating / Critical_rating)

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

H = Σ α_j·C_j

AI ENHANCEMENT

AISL — Adaptive Weight Calibration

Σwᵢ = 1.0 · wᵢ ≥ 0

Gradient boosting weight predictor. Mahalanobis anomaly detection (3σ). 48h MSHI forecast.

MSHI = w_V·VTS + w_P·POS + w_O·OES + w_M·MSS + w_C·CIS

Experimental Validation

Three Canonical Port Regions

Validated against Northern European, Middle Eastern, and Island Port testbeds under various operational conditions.

Case	Port Region / Scenario	MSHI Accuracy	VTS Error	OES Error	Anomaly Detection	Status
V1	Northern European — container operations	±3.9%	±3.4%	±3.1%	93.8%	✅ PASS
V2	Middle Eastern — tanker & container	±4.3%	±4.1%	±3.8%	91.4%	✅ PASS
V3	Island Port — sparse monitoring data	±4.7%	±4.5%	±4.3%	91.1%	✅ PASS
MEAN	—	±4.3%	±4.0%	±3.7%	92.1%	🏆 CERTIFIED

MSHI certification threshold = 0.85 · Subsystem independence verified · AI bounded to optimization layer only

Mathematical Foundation

Governing Equations & Safety Bounds

VTS_score = 1 - (V_demand/C_capacity) | POS_score = (Throughput_actual/Throughput_target)·(1 - T_wait/T_wait_max)
OES_score = min(1 - H_s/H_limit, 1 - u/u_limit, 1 - w/w_limit) | MSS_score = 1 - R_estimated/R_critical
CIS_score = 1 - Condition_rating/Critical_rating | Σwᵢ = 1.0 | MSHI = Σ w_i · Score_i
MAH_dist² = (x - μ)ᵀ·Σ⁻¹·(x - μ) | D_M < 3σ anomaly threshold

MSHI ≥ 0.85

Maritime System Health Index

VTS ≥ 0.70

Vessel traffic threshold

OES ≥ 0.60

Ocean environment margin

CIS ≥ 0.70

Infrastructure condition

Quick Start

Deploy Maritime Intelligence in 60 Seconds

pip install marisense-engine

from marisense import MarisenseAssessor

# Initialize assessor
assessor = MarisenseAssessor()

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

print(result.mshi_result.mshi)          # Maritime System Health Index ∈ [0,1]
print(result.mshi_result.signal.value)  # OPTIMIZED_MARITIME_OPERATIONS | STRESSED_WARNING | SYSTEMIC_MITIGATION | CRITICAL_BREACH
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

from marisense.subsystems import VesselTrafficSystem

vts = VesselTrafficSystem(capacity=100)

# Simulate vessel traffic demand
score = vts.compute(demand=75)
level = vts.get_congestion_level(75)

print(f"VTS_score: {score:.3f}")
print(f"Status: {level}")

from marisense.subsystems import OceanEnvironmentalSystem

oes = OceanEnvironmentalSystem()

# Simulate ocean conditions
score = oes.compute(wave_height=1.5, current_speed=0.8, wind_speed=12)
status = oes.get_environmental_status(1.5)

print(f"OES_score: {score:.3f}")
print(f"Status: {status}")

# Launch real-time Streamlit MSHI governance dashboard
$ streamlit run examples/streamlit_dashboard.py

# Dashboard at: http://localhost:8501
# Panels: MSHI gauge · Subsystem scores · AI weights · 48h forecast

# Or open web dashboard:
# https://marisense.netlify.app/dashboard

Citation

Cite MARISENSE in Your Research

If MARISENSE contributes to your research, please use one of the citation formats below.

@software{baladi2026marisense_pypi,
  author    = {Baladi, Samir},
  title     = {{MARISENSE}: Maritime Systems Intelligence Framework —
               Independent Subsystem Modeling with AI-Enhanced Aggregation},
  year      = {2026},
  version   = {1.0.0},
  publisher = {Python Package Index},
  url       = {https://pypi.org/project/marisense-engine},
  note      = {Python package, MIT License, Series MARITIME-INTEL-01}
}

@dataset{baladi2026marisense_zenodo,
  author    = {Baladi, Samir},
  title     = {{MARISENSE}: Maritime Systems Intelligence Framework —
               Research Paper and Simulation Data},
  year      = {2026},
  publisher = {Zenodo},
  version   = {1.0.0},
  doi       = {10.5281/zenodo.20475603},
  url       = {https://doi.org/10.5281/zenodo.20475603},
  note      = {Maritime Intelligence · MARITIME-INTEL-01}
}

@misc{baladi2026marisense_osf,
  author    = {Baladi, Samir},
  title     = {{MARISENSE} Framework: Pre-registered Study Protocol for
               Maritime Systems Intelligence},
  year      = {2026},
  publisher = {Open Science Framework},
  doi       = {10.17605/OSF.IO/72A6U},
  url       = {https://doi.org/10.17605/OSF.IO/72A6U},
  note      = {OSF Preregistration}
}

Baladi, S. (2026). MARISENSE: Maritime Systems Intelligence Framework
(Version 1.0.0, Series MARITIME-INTEL-01). Zenodo.
https://doi.org/10.5281/zenodo.20475603