Centre for Sustainable Agricultural Systems, Institute for Life Sciences and the Environment University of Southern Queensland, Toowoomba, QLD 4350, Australia. Electronic address: Cherie.O'
Centre for Sustainable Agricultural Systems, Institute for Life Sciences and the Environment University of Southern Queensland, Toowoomba, QLD 4350, Australia.
Sci Total Environ. 2022 Feb 25;809:151139. doi: 10.1016/j.scitotenv.2021.151139. Epub 2021 Oct 29.
In hydrological modelling, classification of catchments is a fundamental task for overcoming deficits in observational datasets. Most attention on this issue has focussed on identifying the catchments with similar hydrological responses for streamflow. Yet, effective methods for catchment classification are currently lacking in respect to Dissolved Inorganic Nitrogen (DIN), a water quality constituent that, at increasing concentrations, is threatening nutrient sensitive environments. Pattern recognition, using standard Artificial Neural Network algorithm is applied, as a novel approach to classify datasets that are considered to be suitable proxies for biological and anthropogenic drivers of observed DIN releases. Eleven gauged Great Barrier Reef (GBR) catchments within Queensland Australia are classified using spatial datasets extracted from ecosystem (e.g. original ecosystem responses to biogeographic, land zone, land form, and soil type attributes) and land use maps. To evaluate the performance of the examined spatial datasets as a proxy for deductive classification, the classification process is repeated inductively, using observed DIN and streamflow data from gauging stations. The ANN-PR method is seen to generate the same classification score format for the differing dataset types, and this facilitates a direct comparison for model output for observed data corroborations. The Kruskal-Wallis test for independence, at p > 0.05, identifies the deductive classification approach as a predictor for classification using DIN observations, which lacks an independence from each other at a p value of 0.01 and 0.02. This study concludes that an ANN-PR method can integrate the ecosystem and land use mapping data to deductively classify the GBR catchments into four regions that also have similar patterns of DIN concentrations. Due to the uniform availability of the mapping data, the findings provide a sound basis for further investigations into the transposing of knowledge from gauged catchments to ungauged areas.
在水文学模型中，流域分类是克服观测数据集缺陷的基本任务。大多数人关注的是确定具有相似水文响应的流域，以进行河川流量预测。然而，对于溶解无机氮（DIN）这种水质成分，目前还缺乏有效的流域分类方法。当 DIN 浓度增加时，它会威胁到营养敏感的环境。本研究采用标准人工神经网络算法的模式识别，作为一种新方法，用于对被认为是观测到的 DIN 释放的生物和人为驱动因素的合适代理的数据集进行分类。澳大利亚昆士兰州大堡礁（GBR）的 11 个测量流域使用从生态系统（例如原始生态系统对生物地理、土地带、土地形态和土壤类型属性的响应）和土地利用图中提取的空间数据集进行分类。为了评估检查的空间数据集作为演绎分类的代理的性能，使用从测量站获得的观测 DIN 和河川流量数据，重复进行归纳分类过程。ANN-PR 方法生成的分类评分格式与不同数据集类型相同，这便于对观测数据验证的模型输出进行直接比较。当 p 值大于 0.05 时，Kruskal-Wallis 独立性检验确定了演绎分类方法是 DIN 观测的分类预测指标，这表明在 p 值为 0.01 和 0.02 时，它们彼此之间缺乏独立性。本研究的结论是，ANN-PR 方法可以整合生态系统和土地利用制图数据，对 GBR 流域进行演绎分类，将其分为四个具有相似 DIN 浓度模式的区域。由于制图数据的均匀可用性，这些发现为进一步研究从测量流域到未测量地区的知识转移提供了可靠的基础。
