Explainable Ai Approach With Original Vegetation Data Classifies Spatio-temporal Nitrogen In Flows From Ungauged Catchments To The Great Barrier Reef

This article discusses the use of proxy spatial data to classify ungauged catchments with similar Dissolved Inorganic Nitrogen (DIN) responses. A neural network pattern recognition model (ANN-PR) and explainable artificial intelligence approach (SHAP-XAI) were used to match all ungauged catchments that flow to the Great Barrier Reef to gauged ones. The suitability of the catchment matches was verified using a neural network water quality (ANN-WQ) simulator trained on gauged catchment datasets. The results showed that discriminating training data to DIN regime benefits ANN-WQ simulation performance in unsupervised scenarios. Priority monitoring areas were identified to gain observed data for all DIN regimes in catchments that flow to the Great Barrier Reef, Australia.