Flood Mitigation Project for the Ijssel River

This repository contains the code and resources for a Flood Mitigation Project focused on the Ijssel River. The project aims to explore and implement strategies to mitigate the risk of flooding in the surrounding areas.

Project Overview

The Ijssel River is a major river in the Netherlands that poses a significant flood risk due to its geographical location and the potential for heavy rainfall. This project seeks to develop effective flood mitigation strategies by utilizing data analysis and modeling techniques.

The repository includes three Jupyter Notebooks:

• _OpenExploration_pf1.ipynb: This notebook provides an open exploration of the model, its levers, and uncertainties for problem formulation 1.

• _OpenExploration_pf6.ipynb: This notebook provides an open exploration of the model, its levers, and uncertainties for problem formulation 6.

• directed_search.ipynb: This notebook focuses on implementing a directed search for potential flood mitigation strategies. It uses robust decision making and optimization techniques to identify effective strategies.

Furthermore, one Excel File is included: Report tables - Gelderland.xslx: This file contains the regret indicator calculation and the design for tables used in the report.

Repository Structure

The repository is structured as follows:

final_assignment/data/: This directory contains the relevant datasets used in the project.

models/: If applicable, this directory contains any pre-trained models or model configurations used in the project.

report/: This directory holds the final report.

results/: This directory contains the results of the open exploration and directed search.

Problem Formulation

For the Open Exploration problem formulation 1 and 6 are used. For the directed search only problem formulation 6 is used. Hence, disaggregated results were used in the analysis.

Results

All data files can be found in /results/data. Data was stored in either compressed format or csv.

Getting Started

To run the project code, follow these steps:

Clone the repository to your local machine using Git:

Copy code

git clone https://github.com/alex-dietz/model-based-decision-making.git

Install the necessary dependencies. Ensure that you have Python 3.x and Jupyter Notebook installed. You can use pip to install the required Python packages:

pip install -r requirements.txt

License

The code in this repository is licensed under the MIT License. You are free to modify, distribute, and use the code for both non-commercial and commercial purposes.