| quarrint-package {quarrint} | R Documentation |
Interaction Prediction Between Groundwater and Quarry Extension Using Discrete Choice Models and Artificial Neural Networks
Description
An implementation of two interaction indices between extractive activity and groundwater resources based on hazard and vulnerability parameters used in the assessment of natural hazards. One index is based on a discrete choice model and the other is relying on an artificial neural network.
Details
| Package: | quarrint |
| Type: | Package |
| Version: | 1.0.0 |
| Date: | 2016-11-23 |
| Depends: | R(>= 2.10.0), neuralnet |
| License: | GPL-2 |
This package provides two interactions indices between quarries (extractive activity) and groundwater ressources using two different methodologies, namely the discrete choice models and artificial neural networks. The design of those indices is fully detailed in Barthelemy et al. (2016).
The quarries and the groudwater ressources are described by 6 parameters, each classified into 4 modalities. These parameters are grouped into 2 distinct categories:
-
the geological, hydrogeological and piezometric contexts defining the hazard that a quarry represents;
-
the relative position of the quarry and the water catchments, the production of the catchments and the potential quality of the groundwater characterising the vulnerability of the groundwater resources.
Each of the resulting 3327 physically feasible combinations of these parameters
(out of a theoretical number of 4 6 = 4096 possible combinations) determines one
particular quarry site type. These feasible combination are provided in the
data frame quarries.
Depending on the values of the parameters, the interaction index can then be low, medium, high or very high. The interaction level can then be used to inform a quarry operator on the required level of investigation before considering any extension of the quarry.
The method compute.interaction
provides an interface to compute the 2 interaction indices. It takes as an input
an object of type quarry that can be constructed with the method
quarry.
The discrete choice-based and the neural network-based indices can be
respectively be computed with the functions
compute.dc and
compute.ann also taking as an input an
object of type quarry.
The package also includes the function
train.ann to allow the training of custom
artificial neural network that can be used with the functions
compute.ann and
compute.interaction.
Finally an auxillary function int.in.range
is also provided to determine if a given integer is within a specified range.
Author(s)
Johan Barthelemy, Timoteo Carletti, Louise Collier, Vincent Hallet, Marie Moriame, M. and Annick Sartenaer.
Maintainer: Johan Barthelemy johan@uow.edu.au.
References
Barthelemy, J., Carletti, T., Collier L., Hallet, V., Moriame, M., Sartenaer, A. (2016) Interaction prediction between groundwater and quarry extension using discrete choice models and artificial neural networks Environmental Earth Sciences (in press)
Collier, L., Barthelemy, J., Carletti, T., Moriame, M., Sartenaer, A., Hallet, H. (2015) Calculation of an Interaction Index between the Extractive Activity and Groundwater Resources Energy Procedia 76, 412-420
See Also
neuralnet for training and using artifical
neural network and BIOGEME to estimate discrete choice models
(http://biogeme.epfl.ch/home.html).
Examples
# creating a quarry
q <- quarry(geological.context = 2, hydrogeological.context = 4,
piezometric.context = 1, quarry.position = 4,
production.catchment = 4, quality.catchment = 3)
print(q)
# computing the interaction index
inter.idx <- compute.interaction(q)
print(inter.idx)