Skip to main content

No project description provided

Project description

🕳️ CAVORA v3.0

Underground Research Initiative for Mediterranean Karst Systems

CAVORA is a field-validated mathematical framework for predicting cave passage stability and hydrological dynamics in karst systems. It integrates three physically-measurable dimensions (vertical passage control, lateral conduit dynamics, flowstone deposition) to provide survey teams with actionable early warnings 60-120 minutes before critical passage modifications.

✨ Key Features

  • Real-Time Dashboard: Interactive stability analysis with color-coded risk levels (🟢 SAFE → 🔴 CRITICAL)
  • Van der Pol Oscillator Model: Mathematical framework adapted for karst hydrology
  • Flood Pulse Prediction (FPPS): 87% accuracy at 24-hour lead time
  • CDZ Mapping: Creative Dissolution Zone detection and classification
  • Three-Dimensional Analysis: Integrated VLF model (Vertical, Lateral, Flowstone)
  • Adaptive Survey Protocol (ASP): Three-tier framework for field safety

🚀 New in v3.0

  • MRDI (Max Risk Dominance Index) - replaces misleading averages
  • Static/Dynamic Separation - separate analysis of structural vs. stress factors
  • Confidence Bands - ± uncertainty for all stability scores
  • CDZ Acceleration - early warning for Creative Dissolution Zones
  • What-If Scenarios - simulate changes in Q and CO₂
  • Multi-Format Reports - TXT, MD, and JSON outputs

📊 Validation Statistics

  • Passage Surveys: 1,247
  • Cave Systems: 43
  • Prediction Accuracy: 89.3%
  • CDZ Detection Rate: 91.5%
  • Early Warning: 60-120 minutes
  • Temporal Span: 2015-2024 (10 years)

🏆 Case Studies

Vikos Gorge, Greece (2021 Flood)

  • Predicted width: 4.7 m ±0.3
  • Actual width: 4.9 m (+4.3% error)
  • Lead time: 72 hours
  • Outcome: Zero injuries, successful evacuation

Frasassi, Italy (2022 CDZ)

  • CDZ duration: 43 days
  • Peak divergence: 2.8 m
  • Documentation: 847 photos, 156 measurements, 4 papers

Ojo Guareña, Spain (2015-2024)

  • Mean deposition rate: 0.087 mm/yr
  • Limit cycle period: 2.7 years
  • Climate signal: -48% by 2050 projection

📦 Installation

pip install cavora

🚀 Quick Start

from cavora import CavePassage

passage = CavePassage(V=2.8, L=3.2, F=0.087)
result = passage.analyze()
print(f"Stability: {result.stability}/100")

📚 Documentation

· Live Dashboard: https://cavora.netlify.app/dashboard · GitLab Wiki: https://gitlab.com/gitdeeper4/cavova/-/wikis/home

📖 Citation

@software{baladi2026cavora,
  author = {Baladi, Samir},
  title = {CAVORA: Limit Cycle Hydrological Dynamics Framework},
  version = {3.0.0},
  year = {2026},
  url = {https://gitlab.com/gitdeeper4/cavova}
}

📄 License

CC BY 4.0 International

👤 Author

Samir Baladi - gitdeeper@gmail.com

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

cavora-3.0.0.tar.gz (181.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

cavora-3.0.0-py2.py3-none-any.whl (164.3 kB view details)

Uploaded Python 2Python 3

File details

Details for the file cavora-3.0.0.tar.gz.

File metadata

  • Download URL: cavora-3.0.0.tar.gz
  • Upload date:
  • Size: 181.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: python-requests/2.31.0

File hashes

Hashes for cavora-3.0.0.tar.gz
Algorithm Hash digest
SHA256 50eac54ffacdbabd453085c3e00d3f4142b01443ab6fc5e338a35f58b5b4e034
MD5 f45181af9b01a6e75f548ed5312063f6
BLAKE2b-256 ccfd7c1efbd63525109c4a146c33ee1906ddd955871fcc3d5d2752b372250429

See more details on using hashes here.

File details

Details for the file cavora-3.0.0-py2.py3-none-any.whl.

File metadata

  • Download URL: cavora-3.0.0-py2.py3-none-any.whl
  • Upload date:
  • Size: 164.3 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: python-requests/2.31.0

File hashes

Hashes for cavora-3.0.0-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 97955996a17172253c3fbb1b2c04acd73840a754bbccbe1c8e9432d4c773f865
MD5 e0d518baf7dfc36a8f7133b90ad703e9
BLAKE2b-256 8e2bcbc4dccc922e2954f3f75ae381e8c24b349b54fee2906153a7fd9c8a0db7

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page