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Classification of catchments for nitrogen using Artificial Neural Network Pattern Recognition and spatial data.

Cherie M O'Sullivan1, Afshin Ghahramani1, Ravinesh C Deo2

  • 1Centre for Sustainable Agricultural Systems, Institute for Life Sciences and the Environment University of Southern Queensland, Toowoomba, QLD 4350, Australia.

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Summary

Classifying catchments using ecosystem and land use data with Artificial Neural Networks helps predict Dissolved Inorganic Nitrogen (DIN) levels in Great Barrier Reef catchments, aiding water quality management.

Keywords:
Artificial intelligenceDINDeductive inductive catchment classificationGreat Barrier ReefPattern recognitionWater quality

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Area of Science:

  • Environmental Science
  • Hydrology
  • Water Quality Management

Background:

  • Catchment classification is crucial for hydrological modeling, especially for water quality constituents like Dissolved Inorganic Nitrogen (DIN).
  • Existing methods primarily focus on streamflow, leaving a gap in classifying catchments based on DIN dynamics.
  • Increasing DIN concentrations pose a threat to nutrient-sensitive aquatic environments.

Purpose of the Study:

  • To develop and apply a novel method for classifying catchments based on Dissolved Inorganic Nitrogen (DIN) patterns.
  • To utilize spatial datasets of ecosystem and land use attributes as proxies for biological and anthropogenic drivers of DIN.
  • To evaluate the effectiveness of deductive classification using mapping data against inductive classification using observed DIN and streamflow data.

Main Methods:

  • Applied Artificial Neural Network (ANN) with Pattern Recognition (PR) for catchment classification.
  • Used spatial datasets from ecosystem (biogeographic, land zone, land form, soil type) and land use maps for eleven Great Barrier Reef (GBR) catchments.
  • Validated the classification by comparing deductive (spatial data) and inductive (observed DIN and streamflow) approaches using the Kruskal-Wallis test.

Main Results:

  • The ANN-PR method successfully classified GBR catchments into four distinct regions based on integrated ecosystem and land use data.
  • The deductive classification approach using spatial data was found to be a significant predictor for classification based on observed DIN concentrations.
  • A strong correlation was observed between the classification derived from spatial data and actual DIN patterns, with statistical significance (p < 0.01 and p < 0.02).

Conclusions:

  • An ANN-PR method effectively integrates ecosystem and land use mapping data for deductive catchment classification.
  • This approach can reliably classify Great Barrier Reef catchments into regions with similar Dissolved Inorganic Nitrogen (DIN) concentration patterns.
  • The findings support the transfer of hydrological knowledge from gauged to ungauged catchments due to the availability of mapping data.