Impute Water Temperature in the Swiss River Network Using LSTMs

Switzerland is home to the sources of major European rivers. As the thermal regime of rivers is crucial for the environment, the Federal Office for the Environment has been collecting discharge and water temperature data at 81 river water stations for several decades. However, despite diligent collection 30% of the water temperature data is missing due to various reasons. These missing data are problematic in many ways – for instance, in predicting water temperatures based on different models. To tackle this problem, we propose to use LSTMs for water temperature imputing. In particular, we introduce three different scenarios – depending on the available input data – to impute possible data gaps. Then, we propose several methods for each scenario. For our empirical evaluation, we engineer a novel dataset (with ground truth) by artificially introducing gaps of sizes 2, 10, 30 and 60 days in the middle of 90-day sequences. A rather simple interpolation baseline achieves a competitive RM SE on gaps of two days. For larger gaps, however, this simple method clearly fails, and the novel, far more sophisticated models significantly outperform both interpolation and the current state of the art in this application.