Assessment of Neural Networks for Stream-Water-Temperature Prediction
This work addresses the need for reliable water temperature prediction models to understand ecosystem impacts of climate change, but it is incremental as it focuses on improving assessment methods rather than proposing a new predictive paradigm.
The authors tackled the problem of predicting water temperatures in streams under climate change by evaluating state-of-the-art neural networks on six streams in Germany, finding that standard metrics like RMSE are insufficient and introducing additional analysis methods to assess model robustness and parameter impacts.
Climate change results in altered air and water temperatures. Increases affect physicochemical properties, such as oxygen concentration, and can shift species distribution and survival, with consequences for ecosystem functioning and services. These ecosystem services have integral value for humankind and are forecasted to alter under climate warming. A mechanistic understanding of the drivers and magnitude of expected changes is essential in identifying system resilience and mitigation measures. In this work, we present a selection of state-of-the-art Neural Networks (NN) for the prediction of water temperatures in six streams in Germany. We show that the use of methods that compare observed and predicted values, exemplified with the Root Mean Square Error (RMSE), is not sufficient for their assessment. Hence we introduce additional analysis methods for our models to complement the state-of-the-art metrics. These analyses evaluate the NN's robustness, possible maximal and minimal values, and the impact of single input parameters on the output. We thus contribute to understanding the processes within the NN and help applicants choose architectures and input parameters for reliable water temperature prediction models.