Machine-learning methods for stream water temperature prediction

作者：Moritz Feigl, Katharina Lebiedzinski, Mathew Herrnegger, Karsten Schulz · 发表于：Hydrology and earth system sciences · 年份：2021 · DOI：10.5194/hess-25-2951-2021 · 被引用次数：120 · 研究领域：Hydrological Forecasting Using AI、Hydrology and Watershed Management Studies、Fish Ecology and Management Studies

Water temperature in rivers is a crucial environmental factor with the ability to alter hydro-ecological as well as socio-economic conditions within a catchment. The development of modelling concepts for predicting river water temperature is and will be essential for effective integrated water management and the development of adaptation strategies to future global changes (e.g. climate change). This study tests the performance of six different machine-learning models: step-wise linear regression, random forest, eXtreme Gradient Boosting (XGBoost), feed-forward neural networks (FNNs), and two types of recurrent neural networks (RNNs). All models are applied using different data inputs for daily water temperature prediction in 10 Austrian catchments ranging from 200 to 96 000 km 2 and exhibiting a wide range of physiographic characteristics. The evaluated input data sets include combinations of daily means of air temperature, runoff, precipitation and global radiation. Bayesian optimization is applied to optimize the hyperparameters of all applied machine-learning models. To make the results comparable to previous studies, two widely used benchmark models are applied additionally: linear regression and air2stream. With a mean root mean squared error (RMSE) of 0.55 ∘ C, the tested models could significantly improve water temperature prediction compared to linear regression (1.55 ∘ C) and air2stream (0.98 ∘ C). In general, the results show a very similar performance of the teste...