fads_ai_ffa/CITATION.cff at main · hydrostat/fads_ai_ffa · GitHub

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cff-version: 1.2.0
title: "FADS_AI: Machine-learning support for probability-distribution selection in flood frequency analysis"
message: "If you use this code or the application-ready trained model, please cite the archived software release and the associated article when available."
type: software

authors:
  - family-names: "Fernandes"
    given-names: "Wilson"
    affiliation: "hydro_stat research group"

abstract: "FADS_AI is a reproducible code base and application-ready trained model for machine-learning support in probability-distribution selection for flood frequency analysis. The repository contains the source code required to reproduce a Monte Carlo workflow involving candidate probability distributions, L-moment and LMRD descriptors, goodness-of-fit and information-criterion features, machine-learning training, validation-based model selection, final held-out test evaluation, and an XGBoost application model based on classical L-moment descriptors."

keywords:
  - flood frequency analysis
  - probability distribution selection
  - hydrology
  - L-moments
  - L-moment ratio diagram
  - goodness-of-fit
  - information criteria
  - machine learning
  - XGBoost
  - extreme-value statistics

license: MIT
version: 1.0.0
date-released: "2026-05-01"

repository-code: "https://github.com/hydrostat/fads_ai_ffa"
url: "https://doi.org/10.5281/zenodo.19950473"
doi: "10.5281/zenodo.19950473"

preferred-citation:
  type: software
  title: "FADS_AI: Machine-learning support for probability-distribution selection in flood frequency analysis"
  authors:
    - family-names: "Fernandes"
      given-names: "Wilson"
      affiliation: "hydro_stat research group"
  year: 2026
  version: "1.0.0"
  doi: "10.5281/zenodo.19950473"
  url: "https://doi.org/10.5281/zenodo.19950473"