Soft Computing and Machine Learning in Dam Engineering-Reference-Cited by-同舟云学术

Soft Computing and Machine Learning in Dam Engineering

Published:2023-02-27 Issue:5 Volume:15 Page:917
ISSN:2073-4441
Container-title:Water
language:en
Short-container-title:Water

Author:

Hariri-Ardebili Mohammad Amin¹²^ORCID,Salazar Fernando³^ORCID,Pourkamali-Anaraki Farhad⁴^ORCID,Mazzà Guido⁵,Mata Juan⁶^ORCID

Affiliation:

1. Department of Civil Environmental and Architectural Engineering, University of Colorado Boulder, Boulder, CO 80309, USA

2. College of Computer, Mathematical and Natural Sciences, University of Maryland, College Park, MD 20742, USA

3. International Centre For Numerical Methods In Engineering (CIMNE), P. Gral. Martinez Campos, 41, 28010 Madrid, Spain

4. Department of Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO 80204, USA

5. Italian National Committee on Large Dams, 2300198 Roma, Italy

6. National Laboratory for Civil Engineering (LNEC), Avenida do Brasil, 101, 1700-066 Lisbon, Portugal

Abstract

Dams have played a vital role in human civilization for thousands of years, providing vital resources such as water and electricity, and performing important functions such as flood control [...]

Publisher

MDPI AG

Subject

Water Science and Technology,Aquatic Science,Geography, Planning and Development,Biochemistry

Link

https://www.mdpi.com/2073-4441/15/5/917/pdf

Reference46 articles.

1. Needs for Portfolio Risk Assessment of Aging Dams in the United States;Lall;J. Water Resour. Plan. Manag.,2023

2. Hariri-Ardebili, M.A., Salamon, J., Mazza, G., Tosun, H., and Xu, B. (2020). Advances in Dam Engineering, MDPI. Infrastructures: Edited Book.

3. ICOLD (2020, April 30). World Register of Dams. Available online: https://www.icold-cigb.org/GB/icold/icold.asp.

4. An overview of soft computing;Ibrahim;Procedia Comput. Sci.,2016

5. Bossoney, C. (1995). Research and Development in the Field of Dams.

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