Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2017WR021749 |
The Impact of Landscape Characteristics on Groundwater Dissolved Organic Nitrogen: Insights From Machine Learning Methods and Sensitivity Analysis | |
Wang, B.1,2,3; Hipsey, M. R.2,3; Ahmed, S.1,3; Oldham, C.1,3 | |
2018-07-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2018 |
卷号 | 54期号:7页码:4785-4804 |
文章类型 | Article |
语种 | 英语 |
国家 | Australia |
英文摘要 | The effect of groundwater nutrient inputs on river and estuary water quality and the potential impacts of urbanization on groundwater are central concerns in many coastal areas. It has been previously identified that dissolved organic nitrogen (DON) can be the dominant form of total dissolved nitrogen (TDN) in some aquifers. However, there is a paucity of evidence about the sources and flow paths of DON, relative to inorganic nitrogen in groundwater. DON and dissolved organic carbon/DON were first compared against different landscape variables in this study, and no significant relationships were found. However, the relationships became statistically significant when shallow samples (sampling depth < 10 m) were separated from deep samples. A random forest model and sensitivity analysis were then applied to further our understanding of the ecohydrological drivers and seasonal patterns that shape DON variability. The random forest algorithm was built to classify 171 groundwater wellbores into three classes (low: <0.5 mg/L; medium: 0.5-2.5 mg/L; and high: >2.5 mg/L) which achieved 72% classification accuracy using landscape characteristics, hydrological conditions, and temporal information. The results indicated that the effects of landscapes on sandy shallow groundwater DON were controlled both by certain landscape characteristics and depth to groundwater. A conceptual model of groundwater DON is therefore proposed where the balance of exposure and processing time scales from the surface to groundwater is the critical control on the preservation of landscape signatures; we expect that this conceptual model would be applicable for other sandy, shallow groundwater areas. |
英文关键词 | groundwater dissolved organic nitrogen urbanization machine learning sensitivity analysis |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000442502100034 |
WOS关键词 | SUPPORT VECTOR MACHINES ; RANDOM FORESTS ; FUTURE-DIRECTIONS ; URBAN WATERSHEDS ; RIVER WATER ; LAND-USE ; CATCHMENT ; MODELS ; CLASSIFICATION ; VARIABLES |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/21413 |
专题 | 资源环境科学 |
作者单位 | 1.Univ Western Australia, Dept Civil Min & Environm Engn, Crawley, WA, Australia; 2.Univ Western Australia, UWA Sch Agr & Environm, Crawley, WA, Australia; 3.Cooperat Res Ctr Water Sensit Cities, Clayton, Vic, Australia |
推荐引用方式 GB/T 7714 | Wang, B.,Hipsey, M. R.,Ahmed, S.,et al. The Impact of Landscape Characteristics on Groundwater Dissolved Organic Nitrogen: Insights From Machine Learning Methods and Sensitivity Analysis[J]. WATER RESOURCES RESEARCH,2018,54(7):4785-4804. |
APA | Wang, B.,Hipsey, M. R.,Ahmed, S.,&Oldham, C..(2018).The Impact of Landscape Characteristics on Groundwater Dissolved Organic Nitrogen: Insights From Machine Learning Methods and Sensitivity Analysis.WATER RESOURCES RESEARCH,54(7),4785-4804. |
MLA | Wang, B.,et al."The Impact of Landscape Characteristics on Groundwater Dissolved Organic Nitrogen: Insights From Machine Learning Methods and Sensitivity Analysis".WATER RESOURCES RESEARCH 54.7(2018):4785-4804. |
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