wAIves 🏄

Project Objective :

The objective of this personal project is to develop AI models that predicting Surf conditions based on weather data.
This involves Data Mining, Supervised Learning, and Web Application development.

Key Project Steps :

• Data Mining : Text Scraping, Data Analysis, and Formatting to build a large dataset (4GB)
• Supervised Learning : Training models using TensorFlow and PyTorch libraries
• Web Development : Setting up server logic (each project branch contains a different implementation of this part) and communication with an API (OpenWeatherMap)

Weather Data :

The weather data collected using OpenWeatherMap (and provided to the models) includes: longitude, latitude, temperature, pressure, wind speed, wind direction.
The value inferred by the models: wave height.
For more information, see the Data folder.

Models :

Numerous models have been trained with various parameter variations (epochs, metrics, layers, and datasets).
These tests have identified the models providing the best results.
For more information, see the Models folder.

Online usage :

wAIves

Local Usage :

Linux

Download the project and add an api_key.txt file containing your OpenWeatherMap API key in the wAIves/Python/ folder.

Run the Python script manage_env.py :

$ python3 manage_env.py

Open the index.html file and start surfing !

Windows :

Download the project and add an api_key.txt file containing your OpenWeatherMap API key in the wAIves/Python/ folder.

Run the Python script manage_env.py from the wAIves/Python/ folder.

Open the index.html file and start surfing !

Requirements :

• OpenWeatherMap API key (free)

• Python version	Compatibility
  >= v3.10	✅
  >= v3.9	✅
  >= v3.8	✅
  >= v3.7	✅
  >= v3.6	✅
  >= v3.5	✅
  >= v2.7	✅
  v3.0.*	❌
  v3.1.*	❌
  v3.2.*	❌
  v3.3.*	❌
  v3.4.*	❌

Weather Data Sources :

• NOAA
• NDBC
• Météo France
• SeaDataNet