Numeraical twins and deep neural network to predict groundwater flow

Groundwater is and will increasingly be under threat due to many anthropic stresses like climate changes, population growth in coastal cities, pollution,... It is known that realistic 3D numerical twins of aquifers allows forecasting their groundwater flow and permits to forecast their behavior in regards to different hydrogeological changes. In this project, we built an ensemble of numerical twins of an aquifer located south-east to Montreal, Qc, Canada, using a nested geostatistical workflow in order to optimize a pump and treat plant constrain by multiple environmental indicators. The ensemble permits to obtain a quantitative measure of the uncertainty for each indicator base on the optimization of the ensemble. While these models have proved to be useful operationally speaking, any changes or scenarios that must be tested requires the managers of the resources to hire qualified companies. This prevents the long term use of the numerical twins and reduce their democratisation to the resource management. This motivates the training of a deep neural graph network on the numerical twins. The trained network is able to forecast short term changes of the groundwater flow due to new pumping rates or new pumping wells in less than a minute.