Robust strategies for forest wildfire mitigation under uncertainty
Description
The following is the Python repository to 1) perform an optimization to minimize departure to historical conditions using gurobipy, 2) a LandClim simulation to simulate wildfires, and 3) a second level optimization that maximizes the frequency of units that contribute to optimal scenario optima (obtained with weighted sum method of separate scenarios), and 4) a visualization that illustrates how the contiguity constraint after Shirabe works. This repository includes the input and output data for a publication under the same name. The project includes a description of how to run the models.
Files
Steps to reproduce
Step 1: download the repository Step 2: create a folder called venv Step 3: Get an academic Guroby license, and insert the username and the license id at the decdicated places in the beginning of weighted_sum_optimization.py and single_optimization_model_executions.py Step 4: install the required libraries in your python terminal, go to the venv directory and use the following command: pip install -r requirements.txt Step 5: execute the python file named single_optimization_model_executions.py to reproduce the Figure 3b Step 6: execute the python file named reference_points to reproduce Figure 4, 5 and Figure 6a and 6b. The individual maps can be, however, shown as a sequence of plots and not exactly as shown in the paper. There will be other results shown that are not referred to in the article