Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR024884 |
Application of Machine Learning to Model Wetland Inundation Patterns Across a Large Semiarid Floodplain | |
Karimi, Sara Shaeri1; Saintilan, Neil1; Wen, Li2; Valavi, Roozbeh3 | |
2019-11-08 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
文章类型 | Article;Early Access |
语种 | 英语 |
国家 | Australia |
英文摘要 | Inundation is a primary driver of floodplain ecology. Understanding temporal and spatial variability of inundation patterns is critical for optimum resource management, particularly in striking an appropriate balance between environmental water application and extractive use. Nevertheless, quantifying inundation at the fine resolution required of ecological modeling is an immense challenge in these environments. In this study, Random Forest, a machine learning technique, was implemented to predict the inundation pattern in a section of the Darling River Floodplain, Australia, at a spatial scale of 30 m and daily temporal resolution. The model achieved very good performance with an average accuracy of 0.915 based on the area under the receiver operating characteristic curve over 10 runs of the model in testing data sets. Six variables explained 70% of the total contribution to inundation occurrence, with the most influential being landscape shape (local deviation from global mean elevation), elevation-weighted distance to the river, the magnitude of river flow (10- and 30-day accumulated river discharge), local rainfall, and soil moisture. This approach is applicable to other floodplains across the world where understanding of fine-scale inundation pattern is for operational ecological management and scenario testing. |
英文关键词 | machine learning downsampling sensitivity-specificity sum maximizer inundation regime wetland environmental water |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000495174600001 |
WOS关键词 | SURFACE-WATER EXTENT ; RANDOM-FOREST ; ENVIRONMENTAL FLOWS ; MACQUARIE MARSHES ; SPATIAL PREDICTION ; TIME-SERIES ; RIVER-BASIN ; DYNAMICS ; SCALE ; CLASSIFICATION |
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/223878 |
专题 | 资源环境科学 |
作者单位 | 1.Macquarie Univ, Dept Earth & Environm Sci, Fac Sci & Engn, Sydney, NSW, Australia; 2.Dept Planning Ind & Environm, Sci Div, Sydney, NSW, Australia; 3.Univ Melbourne, Sch Biosci, Parkville, Vic, Australia |
推荐引用方式 GB/T 7714 | Karimi, Sara Shaeri,Saintilan, Neil,Wen, Li,et al. Application of Machine Learning to Model Wetland Inundation Patterns Across a Large Semiarid Floodplain[J]. WATER RESOURCES RESEARCH,2019. |
APA | Karimi, Sara Shaeri,Saintilan, Neil,Wen, Li,&Valavi, Roozbeh.(2019).Application of Machine Learning to Model Wetland Inundation Patterns Across a Large Semiarid Floodplain.WATER RESOURCES RESEARCH. |
MLA | Karimi, Sara Shaeri,et al."Application of Machine Learning to Model Wetland Inundation Patterns Across a Large Semiarid Floodplain".WATER RESOURCES RESEARCH (2019). |
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