Home ›
Entities
› academia
› Predict water quality using an improved deep learning method based on spatiotemporal feature correlated: a case study of the Tanghe Reservoir in China
Predict water quality using an improved deep learning method based on spatiotemporal feature correlated: a case study of the Tanghe Reservoir in China
Research article (Stochastic Environmental Research and Risk Assessment, 2023) · cited 25× · AI/ML
Predict water quality using an improved deep learning method based on spatiotemporal feature correlated: a case study of the Tanghe Reservoir in China
Summary
Predict water quality using an improved deep learning method based on spatiotemporal feature correlated: a case study of the Tanghe Reservoir in China is a scholarly article[1].
Key Facts
Predict water quality using an improved deep learning method based on spatiotemporal feature correlated: a case study of the Tanghe Reservoir in China'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). Predict water quality using an improved deep learning method based on spatiotemporal feature correlated: a case study of the Tanghe Reservoir in China. Retrieved May 24, 2026, from https://4ort.xyz/entity/predict-water-quality-using-an-improved-deep-learning-method-based-on-spatiotemporal-feature-correlated-a-case-study-of-
MLA“Predict water quality using an improved deep learning method based on spatiotemporal feature correlated: a case study of the Tanghe Reservoir in China.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/predict-water-quality-using-an-improved-deep-learning-method-based-on-spatiotemporal-feature-correlated-a-case-study-of-.
BibTeX@misc{4ortxyz_predict-water-quality-using-an-improved-deep-learning-method-based-on-spatiotemporal-feature-correlated-a-case-study-of-_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Predict water quality using an improved deep learning method based on spatiotemporal feature correlated: a case study of the Tanghe Reservoir in China}}, year = {2026}, url = {https://4ort.xyz/entity/predict-water-quality-using-an-improved-deep-learning-method-based-on-spatiotemporal-feature-correlated-a-case-study-of-}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Predict water quality using an improved deep learning method based on spatiotemporal feature correlated: a case study of the Tanghe Reservoir in China — https://4ort.xyz/entity/predict-water-quality-using-an-improved-deep-learning-method-based-on-spatiotemporal-feature-correlated-a-case-study-of- (retrieved 2026-05-24)
