Recursive neural networks for application-focussed emulation of groundwater models

With changes in climate and water demand placing increasing pressure on UK groundwater resources, water companies need to be able to rapidly and reliably simulate a wide range of scenarios for precipitation, evapotranspiration, and borehole abstraction. But models derived purely from historical data are unreliable in changing conditions, while physics-based groundwater models require time and expertise to run. Here, we show that a Recursive-Neural-Network-based emulator of a physics-based model can make predictions that are both rapid and reliable, giving water companies a tool for both operational decision-making and long-term planning. We discuss the practical importance of representative training data, user-friendly interfaces, and clear uncertainty communication. Finally, we indicate the broader applicability of our work.