Rangel (2026) A machine learning-Monte Carlo simulation framework to determine the probability of flood flowrates in hydrographic basins

Identification

• Journal: Mendeley Data
• Year: 2026
• Date: 2026-05-09
• Authors: Miguel Orlando Durán Rangel
• DOI: 10.17632/68t7ybmd5s.1

Research Groups

• Not specified (Contributor: Miguel Orlando Durán Rangel)

Short Summary

The study proposes a framework combining machine learning and Monte Carlo simulations to estimate the probability of flood flowrates within hydrographic basins.

Objective

• To develop a methodology using machine learning and Monte Carlo simulations to determine the probability of flood flowrates in river basins.

Study Configuration

• Spatial Scale: Hydrographic basins (general)
• Temporal Scale: Not specified

Methodology and Data

• Models used: Machine Learning, Monte Carlo simulation
• Data sources: Not specified (Dataset provided via Mendeley Data)

Main Results

• Not specified in the provided text.

Contributions

• Development of a combined machine learning-Monte Carlo simulation framework for flood flowrate probability analysis in hydrology.

Funding

• Not specified

Citation

@article{Rangel2026machine,
  author = {Rangel, Miguel Orlando Durán},
  title = {A machine learning-Monte Carlo simulation framework to determine the probability of flood flowrates in hydrographic basins},
  journal = {Mendeley Data},
  year = {2026},
  doi = {10.17632/68t7ybmd5s.1},
  url = {https://doi.org/10.17632/68t7ybmd5s.1}
}