README.md

In cvitolo/amca: Automatic Model Configuration Algorithm (AMCA)

AMCA (R-package)

Automatic Model Configuration Algorithm (AMCA)

This is a data mining procedure based on unsupervised machine learning techniques to automatically configure hydrological conceptual rainfall-runoff models such as FUSE.

To cite this software: Vitolo C., Automatic Model Configuration Algorithm (AMCA, R-package), (2015), GitHub repository, https://github.com/cvitolo/amca, doi: http://dx.doi.org/10.5281/zenodo.15721

Installation

Install and load packages

# Install dependent packages from CRAN and GitHub:
install.packages(c("devtools", "tiger", "qualV"))
library(devtools)
install_github("cvitolo/fuse")

# Install the amca package
install_github("cvitolo/amca")

Rainfall-Runoff modelling using FUSE

As an example, we could combine 50 parameter sets and 4 model structures to generate 200 model simulations.

Sample 50 parameter sets for FUSE, using LHS method

library(fuse)
data("fuse_hydrological_timeseries")

set.seed(123)    
parameters <- fuse::generateParameters(NumberOfRuns = 50)

Choose a list of models to take into account

selectedModels <- c(60, 230, 342, 426) 

Run simulations

library(amca)
amca::MCsimulations(DATA = fuse_hydrological_timeseries,
                    parameters = parameters,
                    deltim = 1/24,
                    warmup = 500,
                    ListOfModels = selectedModels)

Find the best configuration(s) amongst those simulated

Run the AMCA algorithm:

results <- amca(DATA = fuse_hydrological_timeseries,
                parameters = parameters,
                deltim = 1/24,
                warmup = 500,
                selectedModels = selectedModels)

The best configuration is stored in

results$RE

PlotEnsembles(bounds = results$ts$bounds,
              dischargeTable = results$ts$discharges)

Warnings

This package and functions herein are part of an experimental open-source project. They are provided as is, without any guarantee.

Leave your feedback

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cvitolo/amca documentation built on May 28, 2019, 7:49 p.m.