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      Does artificial intelligence modelling have anything to offer traditional management of freshwater food resources?

      Death, Russell G.; Collier, Kevin J.; Hudson, Maui; Canning, Adam; Niessen, Miriam; David, Bruno O.; Catlin, Alicia; Hamer, Mark; Pingram, Mochael
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      Death, R. G., Collier, K. J., Hudson, M., Canning, A., Niessen, M., David, B., … Pingram, M. (2017). Does artificial intelligence modelling have anything to offer traditional management of freshwater food resources? ERI report 107. Hamilton, New Zealand: Environmental Research Institute, University of Waikato.
      Permanent Research Commons link: https://hdl.handle.net/10289/12426
      Abstract
      Management of freshwater systems and the ecosystem services they provide has become a multi-stakeholder activity. This requires information on resources and how to manage them to be disseminated to a wide range of users. While artificial intelligence modelling can provide a powerful tool in managing and understanding resources and their drivers, they can be confusing to many users. In this study, we explored the potential use of two alternative modelling approaches (Boosted Regression Trees (BRT) and Bayesian Belief Networks (BBN)) for managing three species of freshwater mahinga kai species-kākahi or kaeo (freshwater mussel), koura (freshwater crayfish) and tuna (freshwater eel). While the BBN model I sbetter for stakeholder communication, the BRT produced more accurate models for all species. However, variables identified as being important for predicting abundance and biomass of these species were often environmental parameters that cannot be managed to improve yield. The artificial intelligence modelling does provide some accurate linkages between the target species and their environmental drivers. Nevertheless, translating these relationships into management plans remains challenging. The models are clearly not a panacea for better resource management, but provide one more tool that might assist multi-stakeholder understanding of how best to manage freshwater resources.
      Date
      2017
      Type
      Technical Report
      Series
      ERI report
      Report No.
      107
      Publisher
      Environmental Research Institute, University of Waikato
      Rights
      © 2017 the authors.
      Collections
      • Science and Engineering Papers [3122]
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