How to deal w___ missing input data

Hydrologic deep learning models have made their way from research to applications. More and more national hydrometeorological agencies, hydro power operators, and consulting companies are building Long Short-Term Memory (LSTM) models for operational use cases. However, all of these efforts are confronted with similar sets of challenges—issues that are different from those in controlled scientific studies. One common issue is the question: how to deal with missing input data? Operational systems depend on the real-time availability of various data products—most notably, meteorological forcings. Additional forcings generally improve the model performance, but at the same time, every new dependency increases the likelihood of an outage in one of the input data products. 

In a recent study, we evaluated different solutions to generate predictions even when some of the meteorological input data do not arrive in time, or not arrive at all (Gauch et al., 2025). In this presentation, we will introduce these methods and discuss how they can help (1) operational forecasters to run reliable real-time flood forecasting systems, and (2) researchers and modelers to build accurate models that leverage as much data as possible.

 

Gauch, M., Kratzert, F., Klotz, D., Nearing, G., Cohen, D., and Gilon, O.: How to deal w___ missing input data, Hydrol. Earth Syst. Sci., 29, 6221–6235, 2025.