https://github.com/atsyplenkov/tidyhydro
Commonly Used Metrics In Hydrological Modelling
https://github.com/atsyplenkov/tidyhydro
Last synced: 2 months ago
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Commonly Used Metrics In Hydrological Modelling
- Host: GitHub
- URL: https://github.com/atsyplenkov/tidyhydro
- Owner: atsyplenkov
- License: other
- Created: 2024-04-18T04:25:25.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2025-02-21T02:17:29.000Z (4 months ago)
- Last Synced: 2025-04-03T12:59:08.322Z (2 months ago)
- Language: R
- Size: 5.66 MB
- Stars: 8
- Watchers: 1
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
README
---
output:
github_document:
html_preview: false
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
requireNamespace("hydroGOF", quietly = TRUE)
requireNamespace("bench", quietly = TRUE)
```
# tidyhydro
[](https://github.com/atsyplenkov/tidyhydro/actions/workflows/R-CMD-check.yaml)
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[](https://CRAN.R-project.org/package=tidyhydro)
The `tidyhydro` package provides a set of commonly used metrics in hydrology (such as _NSE_, _KGE_, _pBIAS_) for use within a [`tidymodels`](https://www.tidymodels.org/) infrastructure. Originally inspired by the [`yardstick`](https://github.com/tidymodels/yardstick/tree/main)and [`hydroGOF`](https://github.com/hzambran/hydroGOF) packages, this library is mainly written in C++ and provides a very quick estimation of desired goodness-of-fit criteria.
Additionally, you'll find here a C++ implementation of lesser-known yet powerful metrics used in reports from the United States Geological Survey (USGS) and the National Environmental Monitoring Standards (NEMS) guidelines. Examples include _PRESS_ (Prediction Error Sum of Squares), _SFE_ (Standard Factorial Error), and _MSPE_ (Model Standard Percentage Error) and others. Based on the equations from _Helsel et al._ ([2020](http://pubs.er.usgs.gov/publication/tm4A3)), _Rasmunsen et al._ ([2008](https://pubs.usgs.gov/tm/tm3c4/)), _Hicks et al._ ([2020](https://www.nems.org.nz/documents/suspended-sediment)) and etc. (see functions documentation for details).
## Example
The `tidyhydro` package follows the philosophy of [`yardstick`](https://github.com/tidymodels/yardstick/tree/main) and provides S3 class methods for vectors and data frames. For example, one can estimate `NSE` and `pBIAS` for a data frame like this:
```{r example}
library(yardstick)
library(tidyhydro)
data(solubility_test)
nse(solubility_test, solubility, prediction)
pbias(solubility_test, solubility, prediction)
```
or create a [`metric_set`](https://yardstick.tidymodels.org/reference/metric_set.html) and estimate several parameters at once like this:
```{r metricset}
hydro_metrics <- yardstick::metric_set(nse, pbias)
hydro_metrics(solubility_test, solubility, prediction)
```
We do understand that sometimes one needs a qualitative interpretation of the model; therefore, we populated every function with a `performance` argument. When `performance = TRUE`, the metric interpretation will be returned according to Moriasi et al. ([2015](https://elibrary.asabe.org/abstract.asp?aid=46548&t=3&dabs=Y&redir=&redirType=)).
```{r interpretation}
hydro_metrics(solubility_test, solubility, prediction, performance = TRUE)
```
## Installation
You can install the development version of `tidyhydro` from [GitHub](https://github.com/atsyplenkov/tidyhydro) with:
``` r
# install.packages("devtools")
devtools::install_github("atsyplenkov/tidyhydro")
# OR
# install.packages("pak")
pak::pak("atsyplenkov/tidyhydro")
```
## Benchmarking
Since the package uses `Rcpp` in the background, it performs slightly faster than base R and other R packages:
```{r benchmarking}
x <- runif(10^5)
y <- runif(10^5)
nse <- function(truth, estimate, na_rm = TRUE) {
1 -
(
sum((truth - estimate)^2, na.rm = na_rm) /
sum((truth - mean(truth, na.rm = na_rm))^2, na.rm = na_rm)
)
}
bench::mark(
tidyhydro = tidyhydro::nse_vec(truth = x, estimate = y),
hydroGOF = hydroGOF::NSE(sim = y, obs = x),
baseR = nse(truth = x, estimate = y),
check = TRUE,
relative = TRUE,
iterations = 100L
)
```
## See also
- [`hydroGOF`](https://github.com/hzambran/hydroGOF) - Goodness-of-fit functions for comparison of simulated and observed hydrological time series