Interaction prediction between groundwater and quarry extension using discrete choice models and artificial neural networks

TL;DR

This paper develops two predictive indices using discrete choice models and neural networks to assess the impact of quarry activities on groundwater resources, aiding feasibility studies for quarry extension projects.

Contribution

It introduces two novel indices based on hazard and vulnerability parameters, combining probabilistic and deterministic approaches for interaction prediction.

Findings

Both models accurately predict interaction levels.

The indices identify high interaction potential for specific quarries.

Application demonstrates practical use in quarry management.

Abstract

Groundwater and rock are intensively exploited in the world. When a quarry is deepened the water table of the exploited geological formation might be reached. A dewatering system is therefore installed so that the quarry activities can continue, possibly impacting the nearby water catchments. In order to recommend an adequate feasibility study before deepening a quarry, we propose two interaction indices between extractive activity and groundwater resources based on hazard and vulnerability parameters used in the assessment of natural hazards. The levels of each index (low, medium, high, very high) correspond to the potential impact of the quarry on the regional hydrogeology. The first index is based on a discrete choice modelling methodology while the second is relying on an artificial neural network. It is shown that these two complementary approaches (the former being probabilistic while the latter fully deterministic) are able to predict accurately the level of interaction. Their use is finally illustrated by their application on the Boverie quarry and the Tridaine gallery located in Belgium. The indices determine the current interaction level as well as the one resulting from future quarry extensions. The results highlight the very high interaction level of the quarry with the gallery.