2nd Prize of the Year 2023
Dr. Yueling Ma
Machine learning for monitoring groundwater resources over Europe
IBG-3: Agrosphere, Research Center Jülich
Dr Yueling Ma's research focuses on the importance of groundwater as a vital resource for Europe and the world. With the increasing threat of extreme weather and climate phenomena, as well as human use, it is crucial to monitor and manage groundwater effectively.
To this end, it has developed a pioneering method called "LSTM-TL", which is based on machine learning. This method enables reliable monthly estimates of groundwater table depth (GST) anomalies on a continental scale, even in regions with limited observational data. The method has been successfully implemented across Europe and can be transferred to other regions.
The basic idea behind LSTM-TL lies in the link between groundwater and other compartments of the hydrological cycle such as atmospheric conditions and soil moisture. By using anomalies of precipitation and soil moisture as inputs, LSTM-TL can generate accurate GST anomalies that can serve as an alternative to direct measurements.
The application of LSTM-TL has proven to be highly reliable and offers numerous advantages. The GST anomalies generated allow a better assessment of historical groundwater dynamics in Europe and show seasonal trends in different regions. This contributes to an improved understanding and enables more effective groundwater management.
In addition to historical analysis, LSTM-TL also offers the possibility of online monitoring and forecasting of groundwater levels. This function opens up new possibilities for groundwater management not only in Europe, but worldwide.
The award-winning research represents a significant advance in reducing the vulnerability of groundwater systems in the context of climate change and human use. The LSTM-TL method could lead to a fundamental change in the way we monitor and manage groundwater.
The award for this young scientist underlines the potential of her work and the valuable contribution she makes to solving global challenges. Her method LSTM-TL has the potential to lay the foundation for future studies and innovations in the field of groundwater management.
