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Machine learning-guided field site selection for river classification

delete2025-07-23
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PRE
AI
Z
Zhihao Wang *
G
G. B. Pasternack
Y
Yufang Jin
C
C Rampini
S
Serena Alexander
N
Nikhil Kumar
R
Rune Storesund
K
Kathleen Perales
C
Christopher Lim
S
Stephanie A. Moreno
I
Igor Laćan
DOI:10.1016/j.jag.2025.104742delete
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Abstract

Abstract

En 中文
• AI can help experts locate best spots to sample nature. • Human–in–the–loop ML framework optimizes reach-scale field site selection. • High-uncertainty field sites capture previously unrecognized stream types. • Replacement method preserves geomorphic characteristics for inaccessible sites.
Keywords:
River classification
Machine learning
Field site selection
Prior datasets
Uncertainty information

Journal

International Journal of Applied Earth Observation and Geoinformation cover
International Journal of Applied Earth Observation and Geoinformation
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8.6
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C
contra costa resource conservation district
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north santa clara resource conservation district
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safer3
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university of california
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Northeastern University
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napa county resource conservation district
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san josé state university
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san mateo/san francisco counties
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