Explainable AI approach with original vegetation data classifies spatio-temporal nitrogen in flows from ungauged catchments to the Great Barrier Reef
Article
O’Sullivan, Cherie M., Deo, Ravinesh C. and Ghahramani, Afshin. 2023. "Explainable AI approach with original vegetation data classifies spatio-temporal nitrogen in flows from ungauged catchments to the Great Barrier Reef." Scientific Reports. 13 (1). https://doi.org/10.1038/s41598-023-45259-0
Article Title | Explainable AI approach with original vegetation data classifies spatio-temporal nitrogen in flows from ungauged catchments to the Great Barrier Reef |
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ERA Journal ID | 201487 |
Article Category | Article |
Authors | O’Sullivan, Cherie M., Deo, Ravinesh C. and Ghahramani, Afshin |
Journal Title | Scientific Reports |
Journal Citation | 13 (1) |
Article Number | 18145 |
Number of Pages | 16 |
Year | 2023 |
Publisher | Nature Publishing Group |
Place of Publication | United Kingdom |
ISSN | 2045-2322 |
Digital Object Identifier (DOI) | https://doi.org/10.1038/s41598-023-45259-0 |
Web Address (URL) | https://www.nature.com/articles/s41598-023-45259-0 |
Abstract | Transfer of processed data and parameters to ungauged catchments from the most similar gauged counterpart is a common technique in water quality modelling. But catchment similarities for Dissolved Inorganic Nitrogen (DIN) are ill posed, which affects the predictive capability of models reliant on such methods for simulating DIN. Spatial data proxies to classify catchments for most similar DIN responses are a demonstrated solution, yet their applicability to ungauged catchments is unexplored. We adopted a neural network pattern recognition model (ANN-PR) and explainable artificial intelligence approach (SHAP-XAI) to match all ungauged catchments that flow to the Great Barrier Reef to gauged ones based on proxy spatial data. Catchment match suitability was verified using a neural network water quality (ANN-WQ) simulator trained on gauged catchment datasets, tested by simulating DIN for matched catchments in unsupervised learning scenarios. We show that discriminating training data to DIN regime benefits ANN-WQ simulation performance in unsupervised scenarios ( p< 0.05). This phenomenon demonstrates that proxy spatial data is a useful tool to classify catchments with similar DIN regimes. Catchments lacking similarity with gauged ones are identified as priority monitoring areas to gain observed data for all DIN regimes in catchments that flow to the Great Barrier Reef, Australia. |
Keywords | AI; Dissolved Inorganic Nitrogen (DIN) |
Related Output | |
Is part of | Artificial intelligence informed simulation of dissolved Inorganic Nitrogen from ungauged catchments to the Great Barrier Reef |
ANZSRC Field of Research 2020 | 401703. Energy generation, conversion and storage (excl. chemical and electrical) |
Public Notes | This article is part of a UniSQ Thesis by publication. See Related Output. |
Byline Affiliations | University of Southern Queensland |
School of Mathematics, Physics and Computing | |
Centre for Applied Climate Sciences | |
Queensland Government, Queensland |
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