KnoX method, or Knowledge eXtraction from neural network model. Case study on the Lez karst aquifer (southern France)

• A novel methodology is proposed to extract information from a neural network model.
• Time transfers can be estimated accurately in non-linear way.
• Neural networks can be used to better understand complex aquifers.

SummaryAs it is the case with many karst aquifers, the Lez basin (southern France) is heterogeneous and thus difficult to model. Due to its supply of fresh water and its ability to reduce flooding however, more in-depth knowledge of basin behavior has proven critical. In addressing this challenging issue, an original methodology based on neural networks is presented herein so as to better understand the hydrodynamic behavior of such systems. Dedicated architecture containing several sub-networks, each being associated to a specific “homogeneous” geological zone that corresponds to a sub-basin contributing discharge, is described. A method, previously proposed for variable selection, has been applied to determine both the relative contribution of the considered zone and its response time. Given the difficulty of verifying such non-observable knowledge, a specific validation step has also been provided. This methodology has been successfully applied to the difficult case of the Lez karst basin, yielding improved knowledge on basin behavior and a revised delimitation of its feeding basin. A new approach has been adopted for the basin, leading the way to additional fieldwork and revised methodologies, particularly regarding the protection of water supply. It should be emphasized that the proposed methodology is generic and applicable to all kinds of aquifers with available and sufficient rainfall and discharge data.