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Fingerprint Fingerprint is based on mining the text of the person's scientific documents to create an index of weighted terms, which defines the key subjects of each individual researcher.

Water distribution systems Engineering & Materials Science
pipe Earth & Environmental Sciences
Water Engineering & Materials Science
urban drainage Earth & Environmental Sciences
water Social Sciences
Patient rehabilitation Engineering & Materials Science
distribution system Social Sciences
Electric power distribution Engineering & Materials Science

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 1990 2019

  • 4788 Citations
  • 19 h-Index
  • 117 Article
  • 50 Conference contribution
  • 1 Comment/debate
  • 1 Editorial

Development of a Reliability Index Considering Flood Damage for Urban Drainage Systems

Lee, E. H. & Kim, J. H., 2019 Jan 1, (Accepted/In press) In : KSCE Journal of Civil Engineering.

Research output: Contribution to journalArticle

Flood damage
Drainage
Rain
Runoff

Modeling the compressive strength of high-strength concrete: An extreme learning approach

Al-Shamiri, A. K., Kim, J. H., Yuan, T. F. & Yoon, Y. S., 2019 May 30, In : Construction and Building Materials. 208, p. 204-219 16 p.

Research output: Contribution to journalArticle

Compressive strength
Learning systems
Concretes
Neural networks
Backpropagation algorithms

Real-time integrated operation for urban streams with centralized and decentralized reservoirs to improve system resilience

Lee, E. H., Choi, Y. H. & Kim, J. H., 2019 Jan 2, In : Water (Switzerland). 11, 1, 69.

Research output: Contribution to journalArticle

urban drainage
Drainage
resilience
drainage
urban areas
2 Citations (Scopus)

Application of flood nomograph for flood forecasting in urban areas

Lee, E. H., Kim, J. H., Choo, Y. M. & Jo, D. J., 2018 Jan 10, In : Water (Switzerland). 10, 1, 53.

Research output: Contribution to journalArticle

Nomograms
flood forecasting
urban areas
natural disaster
urban area
1 Citation (Scopus)

Assessment of machine learning techniques for monthly flow prediction

Alizadeh, Z., Yazdi, J., Kim, J. H. & Al-Shamiri, A. K., 2018 Nov 17, In : Water (Switzerland). 10, 11, 1676.

Research output: Contribution to journalArticle

artificial intelligence
Learning systems
neural network
neural networks
Water Resources