Home ›
Entities
› academia
› Monitor water quality through retrieving water quality parameters from hyperspectral images using graph convolution network with superposition of multi-point effect: A case study in Maozhou River
Monitor water quality through retrieving water quality parameters from hyperspectral images using graph convolution network with superposition of multi-point effect: A case study in Maozhou River
Research article (Journal of Environmental Management, 2023) · cited 38× · AI/ML
Monitor water quality through retrieving water quality parameters from hyperspectral images using graph convolution network with superposition of multi-point effect: A case study in Maozhou River
Summary
Monitor water quality through retrieving water quality parameters from hyperspectral images using graph convolution network with superposition of multi-point effect: A case study in Maozhou River is a scholarly article[1].
Key Facts
Monitor water quality through retrieving water quality parameters from hyperspectral images using graph convolution network with superposition of multi-point effect: A case study in Maozhou River's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Monitor water quality through retrieving water quality parameters from hyperspectral images using graph convolution network with superposition of multi-point effect: A case study in Maozhou River. Retrieved May 24, 2026, from https://4ort.xyz/entity/monitor-water-quality-through-retrieving-water-quality-parameters-from-hyperspectral-images-using-graph-convolution-netw
MLA“Monitor water quality through retrieving water quality parameters from hyperspectral images using graph convolution network with superposition of multi-point effect: A case study in Maozhou River.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/monitor-water-quality-through-retrieving-water-quality-parameters-from-hyperspectral-images-using-graph-convolution-netw.
BibTeX@misc{4ortxyz_monitor-water-quality-through-retrieving-water-quality-parameters-from-hyperspectral-images-using-graph-convolution-netw_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Monitor water quality through retrieving water quality parameters from hyperspectral images using graph convolution network with superposition of multi-point effect: A case study in Maozhou River}}, year = {2026}, url = {https://4ort.xyz/entity/monitor-water-quality-through-retrieving-water-quality-parameters-from-hyperspectral-images-using-graph-convolution-netw}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Monitor water quality through retrieving water quality parameters from hyperspectral images using graph convolution network with superposition of multi-point effect: A case study in Maozhou River — https://4ort.xyz/entity/monitor-water-quality-through-retrieving-water-quality-parameters-from-hyperspectral-images-using-graph-convolution-netw (retrieved 2026-05-24)
