Enhancing River Nutrient Predictions with Extreme Weather Indices and DL-Physical Hybrid Structures for Improved Interpretability

Accurate nutrient predictions are crucial for river water quality management. While deep learning (DL) has shown promise in various Earth science applications, challenges such as data scarcity and limited interpretability hinder its use in river nutrient predictions. Building on insights into the physical dynamics of nutrients, this research investigates how incorporating extreme weather indices as additional input data, which are often overlooked in current DL-based nutrient prediction, could affect model performance. Additionally, we aim to improve model interpretability by developing hybrid DL-physical structures and identify the optimal structure for predicting nutrient indicators. 
 
The study proposes an assessment workflow and demonstrates its application by predicting dissolved inorganic nitrogen (DIN) and soluble reactive phosphorus (SRP) concentrations at the outlet of the Salmons Brook catchment, UK, where nutrient observations are scarce. The workflow includes two key decisions: selecting the input dataset and defining the DL-physical hybrid structure, each with two options. Comparing multiple predictions generated from all decision combinations enables the evaluation of the impacts of extreme weather events and different hybrid structures. 
 
The simulations demonstrate that incorporating extreme weather indices as additional inputs enhanced performance for both nutrient indicators, particularly in capturing extreme values. Overall, the choice of input dataset had a greater impact on the simulations than the hybrid structure, highlighting the importance of careful input selection and preprocessing in DL model development. Integrating results from a physical model into a DL model can improve simulation interpretability by introducing nutrient-related physical processes. In addition to the hybrid structure, incorporating insights into the physical behaviour of nutrients further enhances the interpretability of DL-based predictions, which is crucial for gaining the trust of domain experts, especially when validating results.