Home ›
Entities
› academia
› Deep learning, geometric characterization and hydrodynamic modeling for assessing sewer defect impacts on urban flooding: A case study in Guangzhou, China
Deep learning, geometric characterization and hydrodynamic modeling for assessing sewer defect impacts on urban flooding: A case study in Guangzhou, China
Research article (Journal of Environmental Management, 2023) · cited 11× · AI/ML
Deep learning, geometric characterization and hydrodynamic modeling for assessing sewer defect impacts on urban flooding: A case study in Guangzhou, China
Summary
Deep learning, geometric characterization and hydrodynamic modeling for assessing sewer defect impacts on urban flooding: A case study in Guangzhou, China is a scholarly article[1].
Key Facts
Deep learning, geometric characterization and hydrodynamic modeling for assessing sewer defect impacts on urban flooding: A case study in Guangzhou, 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). Deep learning, geometric characterization and hydrodynamic modeling for assessing sewer defect impacts on urban flooding: A case study in Guangzhou, China. Retrieved May 24, 2026, from https://4ort.xyz/entity/deep-learning-geometric-characterization-and-hydrodynamic-modeling-for-assessing-sewer-defect-impacts-on-urban-flooding-
MLA“Deep learning, geometric characterization and hydrodynamic modeling for assessing sewer defect impacts on urban flooding: A case study in Guangzhou, China.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/deep-learning-geometric-characterization-and-hydrodynamic-modeling-for-assessing-sewer-defect-impacts-on-urban-flooding-.
BibTeX@misc{4ortxyz_deep-learning-geometric-characterization-and-hydrodynamic-modeling-for-assessing-sewer-defect-impacts-on-urban-flooding-_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Deep learning, geometric characterization and hydrodynamic modeling for assessing sewer defect impacts on urban flooding: A case study in Guangzhou, China}}, year = {2026}, url = {https://4ort.xyz/entity/deep-learning-geometric-characterization-and-hydrodynamic-modeling-for-assessing-sewer-defect-impacts-on-urban-flooding-}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Deep learning, geometric characterization and hydrodynamic modeling for assessing sewer defect impacts on urban flooding: A case study in Guangzhou, China — https://4ort.xyz/entity/deep-learning-geometric-characterization-and-hydrodynamic-modeling-for-assessing-sewer-defect-impacts-on-urban-flooding- (retrieved 2026-05-24)
