Maritime Systems Intelligence: Independent Subsystem Modeling and AI-Assisted Aggregation for the Maritime System Health Index
Vessel Traffic · Port Operations · Ocean Environment · Maritime Safety · Coastal Infrastructure · AI-Weighted Maritime System Health Index (MSHI)
MARISENSE is a Maritime Systems Intelligence framework for the continuous modeling, analysis, and safety governance of five analytically independent maritime subsystems — Vessel Traffic, Port Operations, Ocean Environment, Maritime Safety, and Coastal Infrastructure — aggregated into a single composite Maritime System Health Index (MSHI) via an AI-assisted dynamic weighting mechanism. AI serves strictly as a bounded optimization layer at the aggregation interface and at individual subsystem calibration functions, without replacing domain-specific subsystem knowledge or merging subsystem states at the modeling level.
"The maritime domain is simultaneously one of the oldest and most continuously evolving arenas of human infrastructure management. Approximately 90% of global trade by volume moves by sea. MARISENSE maintains five analytically independent maritime subsystems — each governed by its domain-specific equations and calibrated independently against subsystem-specific data. The AI-assisted layer operates exclusively at the aggregation interface, without merging the internal logic of any subsystem model."
Conventional composite maritime indicators (LSCI, Port Performance Index) are computed from annual survey data, focus on efficiency rather than safety governance, and aggregate across ports rather than providing real-time health assessment for a specific maritime domain. MARISENSE addresses all three limitations through its real-time monitoring integration, safety-governance orientation, and site-specific model calibration:
| Signal | Maritime System Status | Action |
|---|---|---|
| 🟢 OPTIMIZED MARITIME OPERATIONS | MSHI ≥ 0.85 |
All subsystems within operational design bounds — standard monitoring, update predictive calibration |
| 🟠 STRESSED SUBSYSTEM WARNING | 0.70 ≤ MSHI < 0.85 |
One or more subsystems showing elevated stress — activate targeted anomaly detection |
| 🟠 SYSTEMIC MITIGATION PHASE | 0.55 ≤ MSHI < 0.70 |
Multiple subsystem degradation — apply cross-subsystem mitigation protocols |
| 🔴 CRITICAL MARITIME BREACH | MSHI < 0.55 |
Safety threshold breached — emergency maritime intervention, port authority coordination |
Note: Safety scores below f(Ŝ₄) = 0.40 trigger Critical Maritime Breach regardless of MSHI composite — maritime safety is non-compensable under the ALARP principle.
- Overview
- Key Features
- Five Independent Maritime Subsystems
- Project Structure
- Quick Start
- MARISENSE Pipeline
- Governing Equations
- Scoring & Safety Bounds
- Platforms & Mirrors
- Clone & Download
- Citation
- License
- Author
- Five analytically independent maritime subsystems — VTS, POS, OES, MSS, CIS — each governed by its own domain-specific equations, calibrated independently against subsystem-specific observational data
- Maritime System Health Index (MSHI) — AI-assisted dynamic weighted composite with four-level governance decision logic, configurable 15-minute to 4-hour update interval
- AI-assisted dynamic weight calibration — constrained quadratic program (CVXPY/OSQP) + gradient boosting weight predictor subject to Σwᵢ = 1, wᵢ ≥ 0
- Mahalanobis composite anomaly detector — 92.1% sensitivity for maritime stress events at 24–48 hour warning horizons
- Vessel Traffic: AIS-based macroscopic flow model — vessel density, flow, speed fields at 0.1 NM resolution; 15-minute update cycle
- Port Operations: multi-indicator efficiency model — berth utilization, crane throughput, cargo dwell time, vessel turnaround
- Ocean Environment: hydrodynamic state model — sea state Hs/Tp, current profiles, wave-current interaction, storm surge
- Maritime Safety: ALARP risk model — collision risk, grounding risk, weather routing, IMO FSA framework
- Coastal Infrastructure: PIANC WG-152 condition rating — multi-attribute C_j rating from 1 (excellent) to 5 (critical)
- Three port-region validation — Northern European gateway, Southeast Asian transshipment hub, Middle Eastern energy terminal
- ±4.3% MSHI accuracy — validated against independent maritime condition assessments across three contrasting port regions
- Full open-source distribution — available across 11 platforms
| # | Subsystem | Acronym | Output Score | Core Governing Method |
|---|---|---|---|---|
| 1 | Vessel Traffic System | VTS | f(Ŝ₁) |
AIS macroscopic flow model · LWR theory · traffic density kᵢ |
| 2 | Port Operations System | POS | f(Ŝ₂) |
Berth utilization + crane throughput + DEA efficiency |
| 3 | Ocean Environmental System | OES | f(Ŝ₃) |
Hydrodynamic state Hs/Tp · ECMWF wave model · ADCP current |
| 4 | Maritime Safety System | MSS | f(Ŝ₄) |
ALARP risk: R_estimated / R_critical · IMO FSA framework |
| 5 | Coastal Infrastructure System | CIS | f(Ŝ₅) |
PIANC WG-152 condition rating C_j · weighted geometric mean |
Maritime System Health Index:
MSHI(t) = Σᵢ wᵢ(t) · f(Ŝᵢ(t)), subject to Σᵢ wᵢ = 1.0, wᵢ ≥ 0
Dynamic weights calibrated by constrained QP + gradient boosting predictor · Structural independence enforced: Sᵢ computed exclusively from domain-i observational inputs
MARISENSE/
│
├── marisense/ # Core Python package
│ ├── __init__.py # Package entry point & public API
│ ├── pipeline.py # Main MARISENSE assessment pipeline
│ ├── mshi.py # MSHI composite index & governance logic
│ │
│ ├── subsystems/ # Five independent maritime subsystems
│ │ ├── __init__.py
│ │ ├── vts.py # Subsystem 1: Vessel Traffic System
│ │ ├── pos.py # Subsystem 2: Port Operations System
│ │ ├── oes.py # Subsystem 3: Ocean Environmental System
│ │ ├── mss.py # Subsystem 4: Maritime Safety System
│ │ └── cis.py # Subsystem 5: Coastal Infrastructure System
│ │
│ ├── vessel_traffic/ # VTS subsystem internals
│ │ ├── __init__.py
│ │ ├── ais_processor.py # AIS data ingestion, trajectory filtering, QC
│ │ ├── lwr_flow_model.py # LWR macroscopic flow: q = k · v
│ │ ├── density_field.py # Vessel density kᵢ at 0.1 NM grid resolution
│ │ ├── speed_density.py # Speed-density fundamental diagram
│ │ ├── fairway_capacity.py # COLREGS Rule 9 fairway capacity model
│ │ ├── tidal_correction.py # Tidal current modification of flow-density
│ │ ├── anomaly_vts.py # A₁(t) trajectory anomaly detection
│ │ └── vts_score.py # f(Ŝ₁) VTS score computation
│ │
│ ├── port_operations/ # POS subsystem internals
│ │ ├── __init__.py
│ │ ├── berth_utilization.py # Berth occupancy ratio and idle time model
│ │ ├── crane_throughput.py # Crane moves per hour productivity tracking
│ │ ├── dea_efficiency.py # Data envelopment analysis port efficiency
│ │ ├── cargo_dwell.py # Cargo dwell time distribution model
│ │ ├── vessel_turnaround.py # Vessel pre-berthing and turnaround time
│ │ ├── congestion_indicator.py # Vessel waiting queue length indicator
│ │ ├── anomaly_pos.py # A₂(t) port operations anomaly detection
│ │ └── pos_score.py # f(Ŝ₂) POS score computation
│ │
│ ├── ocean_environment/ # OES subsystem internals
│ │ ├── __init__.py
│ │ ├── wave_model.py # ECMWF/WAM significant wave height Hs, Tp
│ │ ├── current_profile.py # ADCP current velocity and direction profiles
│ │ ├── wave_current_interaction.py # Wave-current interaction model
│ │ ├── storm_surge.py # Storm surge prediction and tidal integration
│ │ ├── visibility_model.py # Maritime visibility from meteorological data
│ │ ├── sea_state_index.py # Composite sea state operational limit index
│ │ ├── anomaly_oes.py # A₃(t) ocean environment anomaly detection
│ │ └── oes_score.py # f(Ŝ₃) OES score computation
│ │
│ ├── maritime_safety/ # MSS subsystem internals
│ │ ├── __init__.py
│ │ ├── collision_risk.py # Vessel collision risk assessment model
│ │ ├── grounding_risk.py # Grounding risk from chart depth + tide
│ │ ├── alarp_framework.py # ALARP: R_estimated / R_critical model
│ │ ├── imo_fsa.py # IMO FSA formal safety assessment
│ │ ├── weather_routing.py # Weather routing safety envelope
│ │ ├── incident_bayesian.py # Bayesian incident frequency estimation
│ │ ├── anomaly_mss.py # A₄(t) maritime safety anomaly detection
│ │ └── mss_score.py # f(Ŝ₄) MSS score computation (non-compensable)
│ │
│ ├── coastal_infrastructure/ # CIS subsystem internals
│ │ ├── __init__.py
│ │ ├── pianc_rating.py # PIANC WG-152 condition rating C_j (1–5 scale)
│ │ ├── inspection_data.py # Inspection record ingestion and QC
│ │ ├── deterioration_model.py # Predictive deterioration curve model
│ │ ├── consequence_weights.py # MCA consequence weight assignment
│ │ ├── geometric_mean.py # Weighted geometric mean I_score
│ │ ├── sensor_monitoring.py # Continuous structural sensor integration
│ │ ├── anomaly_cis.py # A₅(t) coastal infrastructure anomaly
│ │ └── cis_score.py # f(Ŝ₅) CIS score computation
│ │
│ ├── aggregation/ # AI-assisted MSHI aggregation layer
│ │ ├── __init__.py
│ │ ├── qp_weight_optimizer.py # CVXPY/OSQP constrained QP weight optimizer
│ │ ├── gbm_weight_predictor.py # Gradient boosting weight predictor
│ │ ├── dynamic_weights.py # Context-sensitive w(t) vector
│ │ ├── equal_weight_baseline.py # Equal-weight MSHI baseline (wᵢ = 0.20)
│ │ ├── weight_constraint.py # Σwᵢ = 1.0, wᵢ ≥ 0 enforcement
│ │ └── mshi_computation.py # Final MSHI composite computation
│ │
│ ├── anomaly/ # Anomaly detection layer
│ │ ├── __init__.py
│ │ ├── mahalanobis.py # Mahalanobis composite anomaly A_composite
│ │ ├── subsystem_anomaly.py # Per-subsystem anomaly indicators A₁–A₅
│ │ ├── alert_routing.py # Route alerts to responsible authority
│ │ ├── early_warning.py # 24–48h maritime stress event early warning
│ │ └── stress_event_log.py # Subsystem stress event archival
│ │
│ ├── sensors/ # Maritime data ingestion and fusion
│ │ ├── __init__.py
│ │ ├── ais_receiver.py # AIS Class A/B vessel data (2–10s intervals)
│ │ ├── port_scada.py # Port terminal SCADA operational data
│ │ ├── wave_buoy.py # Moored wave buoy Hs/Tp/Tz data
│ │ ├── adcp_reader.py # ADCP current velocity profiles
│ │ ├── tide_gauge.py # Tidal elevation and storm surge
│ │ ├── metocean_api.py # ECMWF/Copernicus metocean API connector
│ │ └── fusion.py # Multi-source maritime data fusion and QC
│ │
│ ├── forecasting/ # Short-term subsystem forecasting
│ │ ├── __init__.py
│ │ ├── mshi_forecast.py # 24–48h MSHI trajectory projection
│ │ ├── vessel_traffic_forecast.py # Short-term traffic density forecast
│ │ ├── sea_state_forecast.py # 6h-ahead sea state forecast
│ │ └── uncertainty.py # Forecast uncertainty quantification
│ │
│ └── utils/ # Shared utilities
│ ├── __init__.py
│ ├── metrics.py # MSHI, f(Ŝᵢ), A_composite computation
│ ├── validators.py # Input validation and governance bounds
│ └── constants.py # IMO thresholds, ALARP levels, PIANC ratings
│
├── monitoring/ # Real-time monitoring dashboard
│ ├── __init__.py
│ ├── app.py # Streamlit application entry point
│ ├── dashboard.py # MSHI governance dashboard layout
│ ├── subsystem_panel.py # Five-subsystem score display
│ ├── maritime_map.py # Spatial AIS and sensor map
│ ├── weight_tracker.py # Dynamic weight vector trend display
│ └── components/
│ ├── mshi_gauge.py # MSHI composite index gauge display
│ ├── signal_panel.py # 🔴🟠🟢 governance signal status
│ └── forecast_panel.py # 24–48h MSHI trajectory projection
│
├── archival/ # Operational data archival
│ ├── __init__.py
│ ├── writer.py # Append-only JSON/CSV record writer
│ ├── checksum.py # SHA-256 tamper-evidence layer
│ └── partitioner.py # Per-subsystem time-window CSV partitioner
│
├── simulation/ # Validation and benchmark environment
│ ├── __init__.py
│ ├── port_configs.py # Port region geometry and parameter definitions
│ ├── loading_scenarios.py # Traffic, weather, and seasonal scenarios
│ ├── benchmarks.py # Three port-region validation suite
│ ├── parameters.py # Canonical v1.0.0 parameter registry
│ └── results/ # Pre-computed validation outputs
│ ├── V1_N_European_gateway_port.json
│ ├── V2_SE_Asian_transshipment_hub.json
│ └── V3_ME_energy_terminal.json
│
├── examples/ # Usage examples and tutorials
│ ├── quickstart.py # Minimal working example
│ ├── basic_mshi.ipynb # Jupyter: single port-region MSHI assessment
│ ├── dynamic_weights.ipynb # Jupyter: QP + GBM weight calibration
│ ├── ais_vessel_traffic.ipynb # Jupyter: VTS score from AIS data
│ ├── anomaly_detection.ipynb # Jupyter: Mahalanobis composite anomaly demo
│ ├── streamlit_dashboard.py # Launch real-time monitoring dashboard
│ └── safety_non_compensable.py # MSS non-compensable override demo
│
├── tests/ # Unit and integration tests
│ ├── test_vts.py
│ ├── test_pos.py
│ ├── test_oes.py
│ ├── test_mss.py
│ ├── test_cis.py
│ ├── test_mshi.py
│ ├── test_aggregation.py
│ ├── test_anomaly.py
│ └── test_pipeline.py
│
├── docs/ # Documentation source
│ ├── architecture.md # Subsystem architecture reference
│ ├── mathematics.md # Governing equations documentation
│ ├── monitoring.md # Sensor system and data fusion guide
│ ├── governance.md # MSHI threshold calibration reference
│ └── api_reference.md # Full Python API reference
│
├── paper/ # Research paper artifacts
│ ├── MARISENSE_Research_Paper.pdf # Published paper (PDF)
│ ├── MARISENSE_Research_Paper.docx # Editable Word version
│ └── figures/
│ ├── mshi_formulation.svg
│ ├── weight_calibration_diagram.svg
│ ├── five_subsystems_overview.svg
│ └── validation_three_ports.svg
│
├── .gitlab-ci.yml # GitLab CI/CD pipeline
├── .github/
│ └── workflows/
│ ├── tests.yml
│ └── publish.yml
├── pyproject.toml
├── setup.cfg
├── requirements.txt
├── requirements-dev.txt
├── CHANGELOG.md
├── CONTRIBUTING.md
├── CODE_OF_CONDUCT.md
├── AUTHORS.md
├── LICENSE
└── README.md # This file
# Install from PyPI
pip install marisense-engine
# Install from source
git clone https://github.com/gitdeeper13/MARISENSE.git
cd MARISENSE
pip install -e .from marisense import MariSenseAssessor
# Initialize assessor with port-region configuration
assessor = MariSenseAssessor(
port_config="configs/northern_european_gateway.yaml",
sensor_stream="live" # or path to historical CSV
)
# Run full MARISENSE assessment pipeline
result = assessor.evaluate()
print(result.mshi) # Maritime System Health Index ∈ [0, 1]
print(result.signal) # "OPTIMIZED" | "STRESSED" | "MITIGATION" | "CRITICAL"
print(result.vts_score) # Vessel Traffic System score f(Ŝ₁)
print(result.pos_score) # Port Operations System score f(Ŝ₂)
print(result.oes_score) # Ocean Environmental System score f(Ŝ₃)
print(result.mss_score) # Maritime Safety System score f(Ŝ₄)
print(result.cis_score) # Coastal Infrastructure System score f(Ŝ₅)
print(result.weights) # Current dynamic weight vector (w₁, w₂, w₃, w₄, w₅)
print(result.anomaly) # Composite Mahalanobis anomaly score A_compositefrom marisense import MariSenseAssessor
from marisense.subsystems import VTS, POS, OES, MSS, CIS
assessor = MariSenseAssessor(
port_config="configs/northern_european_gateway.yaml",
subsystems={
"vts": VTS(grid_resolution=0.1, ais_class=["A","B"]),
"pos": POS(dea_reference="global_top20_ports"),
"oes": OES(wave_model="ECMWF_ERA5", adcp_depth=20),
"mss": MSS(alarp_level=0.70, imo_fsa=True),
"cis": CIS(pianc_wg=152, inspection_cycle_months=6),
}
)
result = assessor.evaluate()
print(result.breakdown)
# {"vts": 0.84, "pos": 0.91, "oes": 0.88, "mss": 0.79, "cis": 0.86}from marisense.aggregation import QPWeightOptimizer, GBMWeightPredictor
# Train constrained QP optimizer on historical port data
optimizer = QPWeightOptimizer(lambda_reg=0.5)
optimizer.fit(
historical_scores="data/port_a_24months.csv",
independent_assessments="data/port_a_authority_scores.csv"
)
# Train gradient boosting weight predictor on QP-derived training corpus
predictor = GBMWeightPredictor()
predictor.fit(optimizer.training_corpus_)
# Predict context-sensitive weights
w = predictor.predict(
scores=[0.84, 0.91, 0.88, 0.79, 0.86],
anomaly_flags=[0, 0, 0, 1, 0],
meteo_regime="storm_approaching",
season="winter"
)
print(f"Weights: VTS={w[0]:.3f} POS={w[1]:.3f} OES={w[2]:.3f} MSS={w[3]:.3f} CIS={w[4]:.3f}")
print(f"Sum check: {sum(w):.6f}") # Must equal 1.0from marisense.maritime_safety import ALARPFramework
alarp = ALARPFramework(r_acceptable_ratio=0.70)
# Check non-compensable safety override
mss_score = 0.38 # Below 0.40 threshold
result = alarp.evaluate(mss_score)
print(f"Safety score: {mss_score}")
print(f"Non-compensable override: {result.override}") # True
print(f"Governance signal: {result.forced_signal}") # "CRITICAL_MARITIME_BREACH"
print("Note: MSS override applies regardless of MSHI composite value")# Start Streamlit MSHI governance dashboard
streamlit run examples/streamlit_dashboard.py
# Dashboard at: http://localhost:8501
# Panels:
# · MSHI composite gauge with 4-level signal
# · AIS vessel traffic density map
# · Five-subsystem score display
# · Dynamic weight vector trend
# · Mahalanobis anomaly indicators A₁–A₅
# · 24–48h MSHI trajectory forecast┌────────────────────────────────────────────────────────────────────────────────┐
│ Data: AIS Class A/B · Port SCADA · Wave Buoys · ADCP · Tide Gauges · ECMWF │
│ Inspection Records · Incident Databases · Satellite Imagery │
└──────────────────────────┬─────────────────────────────────────────────────────┘
│
┌─────────────────────┼──────────────────────────┐
│ │ │ │ │
▼ ▼ ▼ ▼ ▼
VTS POS OES MSS CIS
LWR Flow DEA ECMWF ALARP PIANC
AIS grid Berth Hs/Tp/Tz R_est/R_crit WG-152
0.1 NM Crane tput Wave-curr. IMO FSA C_j (1–5)
f(Ŝ₁) f(Ŝ₂) f(Ŝ₃) f(Ŝ₄) ≥ 0.40 f(Ŝ₅)
│ │ │ │ │
└─────────┴───────────┴───────────┴──────────────┘
│
AI-Assisted Aggregation Layer
Constrained QP weight optimizer (CVXPY/OSQP)
Gradient boosting weight predictor
Context: meteo regime, season, anomaly flags
Constraint: Σwᵢ = 1.0, wᵢ ≥ 0
Mahalanobis anomaly detection A₁–A₅
Alert routing to responsible authority
24–48h early warning horizon
│
▼
Maritime System Health Index (MSHI)
MSHI(t) = Σᵢ wᵢ(t) · f(Ŝᵢ(t))
Configurable 15-min to 4-hour update
│
┌────────┴────────┐
▼ ▼
Safety Signal Archival
🟢🟠🔴 JSON/CSV + SHA-256
4-level MSHI Streamlit dashboard
| # | Subsystem | Score | Core Method | Primary Data Source |
|---|---|---|---|---|
| 1 | VTS | f(Ŝ₁) |
LWR macroscopic flow + AIS trajectory analysis | AIS Class A/B (2–10s) |
| 2 | POS | f(Ŝ₂) |
DEA efficiency + berth/crane productivity model | Port terminal SCADA |
| 3 | OES | f(Ŝ₃) |
ECMWF/WAM wave model + ADCP current profiles | Wave buoys, ADCP, ECMWF |
| 4 | MSS | f(Ŝ₄) |
ALARP risk model + IMO FSA (non-compensable ≥0.40) | Incident records, AIS |
| 5 | CIS | f(Ŝ₅) |
PIANC WG-152 C_j rating + weighted geometric mean | Inspection records, sensors |
| — | AI | w(t) |
QP optimizer + GBM predictor (Σwᵢ=1) | Historical (context, weights) pairs |
| — | MSHI | MSHI(t) |
Dynamic weighted composite Σwᵢ·f(Ŝᵢ) | All five subsystems |
Eq. 1 — Vessel Traffic LWR Flow Model:
qᵢ = kᵢ · vᵢ (vessels/h = vessels/NM × knots)
v(k) = v_free · [1 − (k / k_jam)^n] (Greenshields speed-density)
Eq. 2 — Maritime Safety ALARP Risk Score:
f(Ŝ₄) = 1 − (R_estimated / R_critical)
Override: if f(Ŝ₄) < 0.40 → MSHI signal = CRITICAL regardless of composite
Eq. 3 — AI Dynamic Weight Optimization (Constrained QP):
min_{w} ‖MSHI(w, f(Ŝᵢ)) − MSHI_independent‖² + λ · ‖w − w_prior‖²
subject to: wᵢ ≥ 0, Σwᵢ = 1.0 (solved via CVXPY/OSQP, λ = 0.5)
Eq. 4 — Coastal Infrastructure Condition Rating (PIANC WG-152):
I_score = 1 − Σⱼ (αⱼ · C_j) / (5 · Σⱼ αⱼ) (C_j ∈ {1,2,3,4,5})
Eq. 5 — Mahalanobis Composite Anomaly:
A_composite = √[(f(Ŝ) − μ)ᵀ · Σ⁻¹ · (f(Ŝ) − μ)]
Eq. 6 — Maritime System Health Index:
MSHI(t) = w₁·f(Ŝ₁) + w₂·f(Ŝ₂) + w₃·f(Ŝ₃) + w₄·f(Ŝ₄) + w₅·f(Ŝ₅)
MSHI governance certification thresholds:
MSHI ≥ 0.85 → 🟢 Optimized Maritime Operations
0.70 ≤ MSHI < 0.85 → 🟠 Stressed Subsystem Warning (Level 1)
0.55 ≤ MSHI < 0.70 → 🟠 Systemic Mitigation Phase (Level 2)
MSHI < 0.55 → 🔴 Critical Maritime Breach
Subsystem-level governance bounds:
f(Ŝ₁) VTS ≥ 0.70 (vessel traffic flow within operational capacity)
f(Ŝ₂) POS ≥ 0.65 (port operations above minimum efficiency threshold)
f(Ŝ₃) OES ≥ 0.60 (sea state within vessel operational limits)
f(Ŝ₄) MSS ≥ 0.40 (NON-COMPENSABLE: triggers CRITICAL regardless of MSHI)
f(Ŝ₅) CIS ≥ 0.70 (infrastructure condition above critical deterioration)
A_composite < 3σ (Mahalanobis anomaly below threshold)
Validation results (MARISENSE v1.0.0):
| Port Region | Profile | MSHI Accuracy | VTS Accuracy | OES Accuracy | Anomaly Detection |
|---|---|---|---|---|---|
| Port A | Northern European gateway — high traffic density | ±4.1% | ±3.7% | ±3.5% | 93.4% |
| Port B | Southeast Asian transshipment hub — container focus | ±4.3% | ±4.1% | ±4.2% | 91.8% |
| Port C | Middle Eastern energy terminal — LNG/crude | ±4.5% | ±3.8% | ±3.9% | 91.2% |
| Mean | — | ±4.3% | ±3.87% | ±3.87% | 92.1% |
| Platform | URL | Role |
|---|---|---|
| 🐙 GitHub (Primary) | github.com/gitdeeper13/MARISENSE | Source code, issues, PRs |
| 🦊 GitLab (Mirror) | gitlab.com/gitdeeper13/MARISENSE | CI/CD mirror |
| 🪣 Bitbucket (Mirror) | bitbucket.org/gitdeeper-13/MARISENSE | Enterprise mirror |
| 🏔️ Codeberg (Mirror) | codeberg.org/gitdeeper13/MARISENSE | Open-source community |
| 📦 PyPI | pypi.org/project/marisense-engine | Python package distribution |
| 🔬 Zenodo | doi.org/10.5281/zenodo.20475603 | Citable DOI, paper & data |
| 📋 OSF Project | osf.io/marisense | Research project registry |
| 📝 OSF Preregistration | doi.org/10.17605/OSF.IO/72A6U | Pre-registered study protocol |
|
||
|
||
| 🌐 Website | marisense.netlify.app | Live documentation & dashboard |
| 🧑🔬 ORCID | orcid.org/0009-0003-8903-0029 | Researcher identity |
| 🗄️ Internet Archive | archive.org/details/osf-registrations-marisense | Permanent archival copy |
| Page | URL |
|---|---|
| Homepage | marisense.netlify.app |
| Dashboard | marisense.netlify.app/dashboard |
| Results | marisense.netlify.app/results |
| Documentation | marisense.netlify.app/documentation |
# GitHub (Primary)
git clone https://github.com/gitdeeper13/MARISENSE.git
# GitLab (Mirror)
git clone https://gitlab.com/gitdeeper13/MARISENSE.git
# Bitbucket (Mirror)
git clone https://bitbucket.org/gitdeeper-13/MARISENSE.git
# Codeberg (Mirror)
git clone https://codeberg.org/gitdeeper13/MARISENSE.git| Source | Link |
|---|---|
| GitHub | MARISENSE-main.zip |
| GitLab | MARISENSE-main.zip |
| Bitbucket | MARISENSE-main.zip |
| Codeberg | MARISENSE-main.zip |
| PyPI files | pypi.org/project/marisense-engine/#files |
| Zenodo record | doi.org/10.5281/zenodo.20475603 |
If MARISENSE contributes to your research, please cite using one of the following formats.
@software{baladi2026marisense_pypi,
author = {Baladi, Samir},
title = {{MARISENSE}: Maritime Systems Intelligence —
Independent Subsystem Modeling and AI-Assisted
Aggregation for the Maritime System Health Index},
year = {2026},
version = {1.0.0},
publisher = {Python Package Index},
url = {https://pypi.org/project/marisense-engine},
note = {Python package, MIT License, Series MARI-INTEL-01}
}@dataset{baladi2026marisense_zenodo,
author = {Baladi, Samir},
title = {{MARISENSE}: Maritime Systems Intelligence —
Independent Subsystem Modeling and AI-Assisted
Aggregation for the Maritime System Health Index —
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 Systems Intelligence · MARI-INTEL-01}
}
@misc{baladi2026marisense_osf,
author = {Baladi, Samir},
title = {{MARISENSE} Framework: Pre-registered Study Protocol
for Maritime Systems Intelligence — Independent
Subsystem Modeling and AI-Assisted MSHI Aggregation},
year = {2026},
publisher = {Open Science Framework},
doi = {10.17605/OSF.IO/72A6U},
url = {https://doi.org/10.17605/OSF.IO/72A6U},
note = {OSF Preregistration}
[](https://doi.org/10.6084/m9.figshare.32525466)
[](https://doi.org/10.6084/m9.figshare.32525466)
}@article{baladi2026marisense,
author = {Baladi, Samir},
title = {{MARISENSE}: Maritime Systems Intelligence —
Independent Subsystem Modeling and AI-Assisted
Aggregation for the Maritime System Health Index},
year = {2026},
month = {May},
version = {1.0.0},
doi = {10.5281/zenodo.20475603},
url = {https://doi.org/10.5281/zenodo.20475603},
note = {Ronin Institute / Rite of Renaissance,
Series MARI-INTEL-01}
}Baladi, S. (2026). MARISENSE: Maritime Systems Intelligence — Independent Subsystem Modeling and AI-Assisted Aggregation for the Maritime System Health Index (Version 1.0.0, Series MARI-INTEL-01). Zenodo. https://doi.org/10.5281/zenodo.20475603
This project is licensed under the MIT License — see the LICENSE file for details.
MIT License
Copyright (c) 2026 Samir Baladi
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
Samir Baladi Researcher — Computational Maritime Systems Analysis & AI-Assisted Infrastructure Assessment Ronin Institute / Rite of Renaissance
| Contact | Link |
|---|---|
| gitdeeper@gmail.com | |
| 🧑🔬 ORCID | 0009-0003-8903-0029 |
| 🐙 GitHub | github.com/gitdeeper13 |
| 🔬 Zenodo | doi.org/10.5281/zenodo.20475603 |
MARI-INTEL-01 · Version 1.0.0 · May 2026
"The maritime domain is simultaneously one of the oldest and most continuously evolving arenas of human infrastructure management. MARISENSE maintains five analytically independent maritime subsystems — each governed by its domain-specific equations — applying AI only in the bounded role of enhancing aggregation accuracy and subsystem calibration, never replacing domain knowledge."
MARISENSE — Maritime Systems Intelligence: Independent Subsystem Modeling and AI-Assisted Maritime System Health Index (MSHI)
Baladi, S. (2026). MARISENSE v1.0.0 | Figshare. https://doi.org/10.6084/m9.figshare.32525466
@misc{baladi2026marisense_figshare,
author = {Samir Baladi},
title = {MARISENSE: Maritime Systems Intelligence —
Independent Subsystem Modeling and AI-Assisted
Maritime System Health Index (MSHI)},
year = {2026},
month = {May},
publisher = {Figshare},
doi = {10.6084/m9.figshare.32525466},
url = {https://doi.org/10.6084/m9.figshare.32525466},
note = {Preprint · Maritime Systems Intelligence · Series MARI-INTEL-01}
}