Water temperature is a vital attribute of physical riverine habitat and one of the focal objectives of river engineering and management. However, in most rivers, there are not enough water temperature measurements to characterize thermal regimes and evaluate its effect on ecosystem functions such as fish migration. To aid in river restoration, machine learning-based algorithms were developed to predict hourly river water temperature. We trained, validated, and tested single-layer and multilayer linear regression (LR) and deep neural network (DNN) algorithms to predict water temperature in the Los Angeles River in southern CA, United States. For the single-layer models, we considered air temperature as the predictive feature, and for the multilayer models, relative humidity, wind speed, and barometric pressure were included in addition to air temperature as the considered features. 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°C), on average, compared to the single-layer models (1.3°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.
Forecasting river water temperature time series using a wavelet–neural network hybrid modelling approach
