BOAR documentation

We present Bayesian Optimization for Automated Roughness calibration in two-dimensional hydrodynamic models (BOAR), an open-source Python framework for automated hydraulic roughness calibration in two-dimensional shallow-water models using BASEMENT. The tool combines Bayesian optimization with conditional sampling to incorporate expert knowledge, such as feasible parameter ranges and inter-parameter constraints, reducing the number of costly model evaluations. BOAR standardizes calibration workflows, improves reproducibility, and supports flexible user-defined loss functions.

Contents

• Quickstart
  • Prerequisites and Installation
  • Configuring BOAR
  • Functionalities
  • Running the Calibration
• Installation
  • Prerequisites
  • Installation Steps
• User Options Configuration
  • General Options
  • BASEMENT Options
  • Simulation Options
  • Optimization Variable Options
  • Sampling Options
  • Surrogate Model Options
  • Full Configuration File
• Loss Function
  • Overview
  • Return Values
  • Example Loss Function implementation:
  • Input Dictionary Structure
  • Utility: utils/find_closest_cell
  • Notes
• Benchmark Functions
  • Ackley Function
  • Rosenbrock Function
  • Compound Channel Benchmark (BASEMENT v4.2)
  • References
• BOAR
  • boar module
  • src package
  • tests package
  • user_defined_configs package

Indices and tables

• Index

• Module Index

• Search Page