Comparison of Machine Learning Techniques and Variables for Groundwater Dissolved Organic Nitrogen Prediction in an Urban Area

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    © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.Dissolved inorganic nitrogen (DIN) are typically the main focus of nutrient management strategies; however, some studies have found that dissolved organic nitrogen (DON) can be the dominant form of total nitrogen (TN) in several Australian estuaries and catchments. To better understand nitrogen cycling and explore the relationships between measured groundwater DON and environmental factors, thirteen machine learning (ML) techniques were compared in this study. DON was simulated under two scenarios using a range of input variables: 1) detailed nutrient data with landscape and sampling factors, and 2) limited nutrient data with landscape and sampling factors. Most of the tested ML algorithms more accurately predicted DON than when it was estimated from the difference between TN and DIN. Some models show greater adaptability to different modelling conditions, with only a few approaches able to predict with high accuracy using limited input variables (scenario 2). From the models tested, bagged mars, cubist and random forest were selected as optimal. Sample depth, sampling date and specific surface water area were the important non-nutrient input variables for DON prediction, which reveals the significant effect of surface environmental factors and seasonality on groundwater DON.
    Original languageEnglish
    Title of host publicationProcedia Engineering
    EditorsD Yoo, H Kim, D Jung, C Song
    Number of pages9
    Publication statusPublished - 2016
    Event12th International Conference on Hydroinformatics : Smart Water for the Future - Songdo ConvensiaIncheon, Korea, Republic of
    Duration: 21 Aug 201626 Aug 2016

    Publication series

    NameProcedia Engineering


    Conference12th International Conference on Hydroinformatics
    Country/TerritoryKorea, Republic of
    CitySongdo ConvensiaIncheon


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