Cetacean-Feeding-Modelling

Table of Contents:

• Description
• Project Structure
• Requirements
• Setup
• Usage
• Contact
• License

Description [to ToC]

Cetacean Feeding Modelling (CFMs) is a Machine Learning-based framework developed to predict cetacean feeding activity in relation to environmental variables in the Central-eastern Mediterranean Sea. CFMs combine behavioral data ('Feeding' vs 'Other' behaviors) with 20 environmental predictors derived from sources such as Copernicus Marine Service (CMS) and EMODnet-bathymetry. By integrating Random Forest and RUSBoost classifier algorithms, the CFMs capture species-specific feeding patterns for three target cetacean species - striped dolphin, common bottlenose dolphin, and Risso's dolphin - in the Gulf of Taranto, our study area, providing a tool for marine conservation and management through predictive feeding maps and offering insights to improve knowledge on feeding habitat characteristics.

Project Structure [to ToC]

The project structure is organized as follows:

• data folder contains two subfolders:

  • raw folder contains two subfolders with the raw dataset:

    • Dataset: contains the Excel file of the raw dataset used to generate the subset used to build the models.
    • Extrapolation: contains the Excel file used to predict feeding habitats of Risso's dolphin for all the Gulf of Taranto, using the bio-chemical model.

  • processed folder contains three subfolders, one for each cetacean species studied:

    • Dataset_grampus: contains five Excel files with the processed datasets related to the Risso's dolphin species, used to run five model with a different variables characterization.
    • Dataset_stenella: contains five Excel files with the processed datasets related to the striped dolphin species,used to run five model with a different variables characterization.
    • Dataset_tursiops: contains five Excel files with the processed datasets related to the common bottlenose dolphin species,used to run five model with a different variables characterization.

• src folder contains the source code files and subfolders:

  • lib folder contains libraries for statistical analysis, pre-processing machine learning, and utility functions.
  • models folder contains the main script for running the ML models.
  • t-test folder contains the main script for running the t-test analysis.
  • extrapolation folder contains the main script for running the best ML models for a target species predicting on a new area.

Requirements [to ToC]

• MATLAB Version 9.14 (R2023a) (https://it.mathworks.com/products/matlab.html)
• Statistics and Machine Learning Toolbox Version 12.5 (R2023a) (https://it.mathworks.com/products/statistics.html)
• Parallel Computing Toolbox Version 7.8 (R2023a) (https://it.mathworks.com/products/parallel-computing.html)

Setup [to ToC]

To set up the project, follow these steps:

1. Clone the repository:

   git clone https://github.com/che7carla/Cetacean-feeding-modelling.git
   

2. Navigate to the project directory:

   cd Cetacean-feeding-modelling
   

Usage [to ToC]

To run the experiment follow these steps:

1. Run the script to train machine learning models:

 \src\models\main_models.m

2. Run the script to do the t-test analysis of the environmental variables for Feeding vs Other behaviors:

\src\t-test\ttest.m

3. Run the script to predict the Risso's dolphin feeding habitats for all the Gulf of Taranto using the M_bio model already trained, on the three summer months of 2024:

\src\extrapolation\Extrapolation_Gulf_of_Taranto.m

Contact [to ToC]

For any questions or inquiries, please contact Carla Cherubini or Rosalia Maglietta

License [to ToC]

This project is licensed under the Apache License 2.0.