aceCitizen science-Large Language Model alliance for automatic assessment of river hydromorphology
Hydromorphological assessment is a fundamental component of river ecosystem evaluation and management, yet standardized field protocols such as the River Habitat Survey (RHS) require trained personnel and substantial field effort. This study investigates whether multimodal large language models (LLMs) can support preliminary hydromorphological screening based solely on photographic documentation of river reaches originating from citizen science datasets. A dataset of 60 river sections previously assessed using t…
A multimodal LLM given photographs of river reaches captured broad gradients of habitat quality and status class, offering a rapid screening aid for prioritizing sites for detailed field survey.
The same LLM approach underestimated human modification of river channels and could not reliably infer submerged channel features, so it cannot replace standardized field surveys for regulatory assessment.
The approach showed only moderate agreement, tested 60 reaches, and could not reliably infer submerged channel features from photographs, so it cannot replace field surveys for regulatory assessment.
Evidence
- Peer-reviewedWater Research2026-10-05
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Truvace Impact Record TRV-2026-1839, v1: “Citizen science-Large Language Model alliance for automatic assessment of river hydromorphology.” Truvace, 2026-10-18. /record/TRV-2026-1839 (accessed at citation time). sha256 00a6710bb82dde13…
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