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Title: Using Remote Sensing and Machine Learning to Locate Groundwater Discharge to Salmon-Bearing Streams
We hypothesized topographic features alone could be used to locate groundwater discharge, but only where diagnostic topographic signatures could first be identified through the use of limited field observations and geologic data. We built a geodatabase from geologic and topographic data, with the geologic data only covering ~40% of the study area and topographic data derived from airborne LiDAR covering the entire study area. We identified two types of groundwater discharge: shallow hillslope groundwater discharge, commonly manifested as diffuse seeps, and aquifer-outcrop groundwater discharge, commonly manifested as springs. We developed multistep manual procedures that allowed us to accurately predict the locations of both types of groundwater discharge in 93% of cases, though only where geologic data were available. However, field verification suggested that both types of groundwater discharge could be identified by specific combinations of topographic variables alone. We then applied maximum entropy modeling, a machine learning technique, to predict the prevalence of both types of groundwater discharge using six topographic variables: profile curvature range, with a permutation importance of 43.2%, followed by distance to flowlines, elevation, topographic roughness index, flow-weighted slope, and planform curvature, with permutation importance of 20.8%, 18.5%, 15.2%, 1.8%, and 0.5%, respectively. The AUC values for the model were 0.95 for training data and 0.91 for testing data, indicating outstanding model performance.  more » « less
Award ID(s):
1702029 1702996 1930451
NSF-PAR ID:
10342679
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
Remote Sensing
Volume:
14
Issue:
1
ISSN:
2072-4292
Page Range / eLocation ID:
63
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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