GSTDTAP  > 资源环境科学
DOI10.1029/2017WR021749
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
ISSN0043-1397
EISSN1944-7973
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
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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
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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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