FIDLAR

Code for the paper "FIDLAR: Forecast-Informed Deep Learning Approaches for Flood Mitigation" accepted by AAAI'25.

Repository description

• data folder includes data sets used
• baseline folder includes baseline models used
• model folder includes our proposed models
• loss folder includes loss functions used
• preprocess folder includes data pre-processing
• postprocess folder includes the programs for experiment results, visualization, and ablation study
• training_WaLeF_models folder includes training programs for Flood Evaluator with all models
• training_optimization_models folder includes training programs for Flood Manager with frozen Flood Evaluator

Requirements

conda create -n env_name python=3.8
conda activate env_name
pip3 install -r requirements.txt

Running

• Download the entire repository and install the required packages (see requirements above).
• For training,
  • Flood Evaluator, go to the training_WaLeF_models folder and run cells in the ipynb files
  • Flood Manager, go to the training_optimization_models folder and run cells in the ipynb files
• For testing and experiment analysis, go to the postprocess folder and run cells in the ipynb files.