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Cooperative Institute for Research to Operations in Hydrology

FIMeval v 1.1: A Python-based engine for evaluation of flood inundation mapping over large-scale Benchmark Database

FIMeval v 1.1: A Python-based engine for evaluation of flood inundation mapping over large-scale Benchmark Database

Day 3 Session 2 (1:30PM)

Presenters:

Dipsikha Devi, The University of Alabama
Supath Dhital, The University of Alabama,

Accurate Flood Inundation Mapping (FIM) is essential for forecasting and evaluation. Traditional pixel-based approaches can be time-intensive and error-prone. Here, we introduced the Flood Inundation Mapping Evaluation Framework (FIMeval), an open-source toolset for large-scale FIM evaluation. FIMeval links to a benchmarking database (FIMbench) that includes high-quality FIM benchmarks across the Contiguous United States, derived from remote sensing and high-fidelity model-predicted datasets. It takes the advantage of comparing multiple target datasets with large benchmark datasets. This package also includes an option to incorporate permanent waterbodies as non-flood pixels with a user input file or pre-set dataset FIMeval supports multiple methods for generating flood extents, allowing users to assess how different delineation techniques influence evaluation outcomes.In addition to conventional performance metrics, FIMeval can quantify the number of inundated buildings based on user-supplied or pre-set building footprint data. New updates in the FIMeval includes different evaluation techniques to test the uncertainties with the performance metrics.

Workshop Slides

Learning Outcomes:

  • Gain experience with the evaluation of flood inundation maps.
  • Learn about evaluating a large number of case studies with the tools developed by presenters.
  • Learn about different evaluation techniques and their impacts on the performance scores

Prerequisites:

  • Knowledge
    • Basic knowledge of hydrology and flood inundation maps.
    • Familiarity with Python language.
      • Basic knowledge of GIS- raster and vector formats, coordinate system.
  • Software
    • Python version 3.10 or greater
    • ArcGIS or QGIS for visualization
  • Accounts
    • GitHub Account
    • CIROH Cloud

Additional Details:

Flood inundation mapping (FIM) is a critical component for model development, disaster preparedness, and risk management. Evaluating FIM with a high-quality benchmark dataset identifies the inconsistencies and discrepancies associated with the model FIM. Evaluating flood maps from different sources using geospatial platforms can be tedious and requires repeated processing and analysis for each map. This manual data processing is cumbersome and prone to human error. Additionally, if numerous case studies exist, manual processing of such case studies is not feasible. Therefore, there is a need for an automatic FIM evaluation technique that will eradicate the mentioned issues. The Python package FIMeval is easy to use, user-friendly, and computationally efficient, which will help the user to evaluate large and scalable datasets effectively.