Comparison of Machine Learning Techniques and Variables for Groundwater Dissolved Organic Nitrogen Prediction in an Urban Area
Comparison of Machine Learning Techniques and Variables for Groundwater Dissolved Organic Nitrogen Prediction in an Urban Area
复制标题
城市地区地下水溶解有机氮预测的机器学习技术和变量比较
DOI：
10.1016/j.proeng.2016.07.527
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
M. Hipsey
中科院分区：
文献类型：
--
作者：
Benya Wang;C. Oldham;M. Hipsey
溶解无机氮(DIN)通常是营养管理策略的主要焦点；然而，一些研究发现，在几个澳大利亚河口和流域，溶解有机氮(DON)可能是总氮(TN)的主要形式。为了更好地了解氮素循环，探索实测地下水DON与环境因素的关系，本研究比较了13种机器学习(ML)技术。DON使用一系列输入变量在两个场景下进行模拟：1)包含景观和采样因子的详细养分数据；2)包含景观和采样因子的有限养分数据。大多数测试的ML算法比从TN和DIN之间的差异估计DON时更准确地预测DON。一些模型表现出对不同建模条件的较大适应性，只有少数方法能够使用有限的输入变量高精度地预测(情景2)。在测试的模型中，袋装火星、立体式森林和随机森林被选为最优模型。采样深度、采样日期和比表面积是DON预测的重要非营养化输入变量，揭示了地表环境因子和季节性对地下水DON的显著影响。
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.