CMCC-Hybrid-EstuaryBoxModel

Table of Contents:

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

Description [to ToC]

The CMCC-Hybrid-EstuaryBoxModel (CMCC-Hybrid-EBM) is an hybrid model for the estimation of the salt-wedge intrusion length (L_x) and the salinity concentration in the estuaries. The Hybrid-EBM has been implemented by combining the ML-based model with the fully-physics EBM model. In particular, the first and the second component of this new model has been obtained replacing the two equations of the fully-physics model by ML algorithms like Random Forest and LSBoost. The Po River (Po-Goro-Branch) has been selected as test-case.

Project Structure [to ToC]

The project structure is organized as follows:

• data folder contains two subfolders:
  • raw folder contains three subfolders with the raw data:
    • Component-1-Lx: Contains Excel files with the raw dataset related to the Component-1 of Hybrid-EBM.
    • Component-2-Ck: Contains three subfolders:
      • Ck-Obs-LSBoost: Contains Excel files with the raw dataset related to the Component-2 of Hybrid-EBM, generated using the Component-1-LSBoost.
      • Ck-Obs-RF: Contains Excel files with the raw dataset related to the Component-2 of Hybrid-EBM, generated using the Component-1-RF.
      • Input-Features-For-Synthetic-Ck-Obs-Generation: Contains Excel files with the raw dataset required to generate the synthetic Ck observations.
    • Component-4-Sul: Contains Excel files with the raw dataset related to the component-4 of hybrid-model.
  • processed folder contains two subfolders with the precessed training and test dataset:
    • Component-1-Lx: Contains Excel files with the processed dataset related to the Component-1 of Hybrid-EBM.
    • Component-2-Ck: Contains two subfolders:
      • Ck-Obs-LSBoost: Contains Excel files with the processed dataset related to the Component-2 of Hybrid-EBM, generated using the Component-1-LSBoost.
      • Ck-Obs-RF: Contains Excel files with the processed dataset related to the Component-2 of Hybrid-EBM, generated using the Component-1-RF.
• src folder contains the source code files and subfolders:
  • models folder contains four subfolders, each of one contains the main scripts for running the modeling and analysis :
    • Component-1-Lx folder includes the following files:
      • train_model_component_1_lx.m: The script to trains ML models for Component-1 of Hybrid-EBM.
    • Component-2-Ck folder includes the following folder and file:
      • train_model_component_2_ck.m: The script to trains ML models for Component-2 of Hybrid-EBM.
      • Component-2-1-Generate-Syntethic-Ck-Observations folder with the files:
        • run_equation_synthetic_ck_observations.m: The script to run creation of new synthetic observations for ck values.
        • generate_synthetic_ck.m: The function (equation) to generate the ck observations.
    • Component-3-Qul folder includes the following files:
      • compute_qul.m: The function (equation) to compute the Component-3 of Hybrid-EBM.
    • Component-4-Sul folder includes the following files:
      • run_experiment_component_4_sul.m: The script to run the Component-4 of Hybrid-EBM.
      • compute_sul.m: The function (equation) to compute the Component-4 of Hybrid-EBM.
  • lib folder contains libraries for analysis, machine learning, and utility functions.
• models folder contains the trained models, models predictions and model summaries for each component of hybrid-model.
• reports folder contains a brief reports of the analysis with graphics and figures.

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 --single-branch --branch master https://github.com/CMCC-Foundation/CMCC-Hybrid-EstuaryBoxModel.git
   

2. Navigate to the project directory:

   cd CMCC-Hybrid-EstuaryBoxModel
   

Usage [to ToC]

To run the experiment follow these steps:

1. Run the script to train machine learning models for the Component-1 of Hybrid-EBM (L_x):

 \src\models\Component-1-Lx\train_model_component_1_lx.m

2. Run the script to generate synthetic observations for the C_k coefficient:

\src\models\Component-2-Ck\Component-2-1-Generate-Syntethic-Ck-Observations\run_equation_synthetic_ck_observations.m

3. Run the script to train machine learning models for the Component-2 of Hybrid-EBM (C_k):

\src\models\Component-2-Ck\train_model_component_2_ck.m

4. Run the script to compute Component-4 of Hybrid-EBM (S_ul):

\src\models\Component-4-Sul\run_experiment_component_4_sul.m

Contact [to ToC]

For any questions or inquiries, please contact Rosalia Maglietta, Leonardo Saccotelli or Giorgia Verri.

License [to ToC]

This project is licensed under the Apache License 2.0.