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.

本研究利用神经网络预测德国六条小溪的水温。通过评估神经网络的鲁棒性、可能的最大值和最小值以及单个输入参数对输出的影响等分析方法，为可靠的水温预测模型选择架构和输入参数做出贡献，以帮助了解神经网络中的过程并增强系统的韧性。