DSAM_test_modData

A moderate dataset containing the rainfall and runoff time series using for
testing data splitting algorithms

datasets

Providing six different algorithms that can be used to split the
available data into training, test and validation subsets with similar distribution
for hydrological model developments. The dataSplit() function will help you divide
the data according to specific requirements, and you can refer to the par.default()
function to set the parameters for data splitting. The getAUC() function will help
you measure the similarity of distribution features between the data subsets.
For more information about the data splitting algorithms, please refer to:
Chen et al. (2022) <doi:10.1016/j.jhydrol.2022.128340>,
Zheng et al. (2022) <doi:10.1029/2021WR031818>.

Junyi Chen

DSAM

Data Splitting Algorithms for Model Developments

Feifei Zheng

DSAM_test_modData function

A data frame with 1000 rows and 5 variables<dl>
<dt>Idex</dt>
<dd>Data subscript that marks the position of each data point</dd><dt>I</dt>
<dd>input vectors</dd><dt>I.1</dt>
<dd>input vectors</dd><dt>I.2</dt>
<dd>input vectors</dd><dt>O</dt>
<dd>The output vector, usually the runoff</dd>...</dl>

Format

Moderate test dataset — DSAM_test_modData

A data frame with 1000 rows and 5 variables<dl>
<dt>Idex</dt>
<dd>Data subscript that marks the position of each data point</dd>

<dt>I</dt>
<dd>input vectors</dd>

<dt>I.1</dt>
<dd>input vectors</dd>

<dt>I.2</dt>
<dd>input vectors</dd>

<dt>O</dt>
<dd>The output vector, usually the runoff</dd>

...

</dl>

DSAM_test_modData: Moderate test dataset

Description

Usage

Arguments

Format