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Application of Machine Learning to Model Wetland Inundation Patterns Across a Large Semiarid Floodplain
Author(s) -
Shaeri Karimi Sara,
Saintilan Neil,
Wen Li,
Valavi Roozbeh
Publication year - 2019
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2019wr024884
Subject(s) - floodplain , hydrology (agriculture) , environmental science , elevation (ballistics) , wetland , digital elevation model , scale (ratio) , spatial ecology , water balance , physical geography , ecology , geography , geology , remote sensing , cartography , geotechnical engineering , biology , geometry , mathematics
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.