Development of a multilayer deep neural network model for predicting hourly river water temperature from meteorological data

We trained the LR and DNN algorithms on Google's TensorFlow model using Keras artificial neural network library on Python. Results showed that multilayer predictions performed better compared to single-layer models by producing mean absolute errors (MAEs), that were 20% smaller (1.05 degrees C), on average, compared to the single-layer models (1.3 degrees C). The multilayer DNN algorithm outperformed the other model where the model's coefficient of determination was 26 and 12% higher compared to the single-layer LR (the base model) and multilayer LR model, respectively.

The multilayer machine learning algorithms, under proper data preparation protocols, may be considered useful tools for predicting water temperatures in sampled and unsampled rivers for current conditions and future estimations affected by different stressors such as climate and land-use change. River temperature predictions from the developed models provide valuable information for evaluating sustainability of river ecosystems and biota.