Hybrid PINN-LSTM Model for River Temperature Prediction: A Physics-Informed Deep Learning Approach

This study proposes a hybrid modeling approach that integrates a Physics Informed Neural Network (PINN) and a long short-term memory (LSTM) network to predict river water temperature in a defined section of the Catu River. The PINN is formulated on the basis of the advection-dispersion-reaction equation to incorporate physical constraints into the learning process, while the LSTM captures temporal patterns from meteorological inputs. The model was trained using normalized historical data and evaluated with standard quantitative metrics and visual comparisons. To examine the influence of each input feature, sensitivity and ablation analyses were performed. The results indicate that the model accurately learns the relevant dependencies, with humidity and dew point exerting the greatest influence on the predictions. In contrast, precipitation showed negligible impact on model performance, aligning with the low and seasonally concentrated rainfall of the region. These results suggest that the model captures both the temporal and physical patterns associated with the thermal dynamics of the river. The hybrid model reproduced observed seasonal variations and extreme temperature peaks with consistency, demonstrating predictive robustness under variable environmental conditions. The combination of data-driven learning and physically constrained modeling offers a viable solution for temperature forecasting in river systems, particularly in regions with limited observational data.