期刊
WATER RESOURCES RESEARCH
卷 55, 期 11, 页码 8765-8778出版社
AMER GEOPHYSICAL UNION
DOI: 10.1029/2019WR024884
关键词
machine learning; downsampling; sensitivity-specificity sum maximizer; inundation regime; wetland; environmental water
资金
- Macquarie University, Australia under the International Macquarie University Research Training Program Scholarship (iMQRTP)
- Australian Government Research Training Program Scholarship
- Rowden White Scholarship
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.
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