Water Distribution Network Perspective in RAFAEL Project, A System for Critical Infrastructure Risk Analysis and Forecast

Longobardi, Antonia; Fattoruso, Grazia; Guarnieri, Guido; Pietro, Antonio Di; Porta, Luigi La; Tofani, Alberto; Pollino, Maurizio

doi:10.1007/978-3-030-87010-2_21

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

2023

Publication Types

Select...

Book2

Article1

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To restore the missing hydrometeorological data, statistical methods are widely used [1,2], while the gaps are replaced by either the average value of this indicator or the missing data is removed from the time series.…”

Section: Introductionmentioning

confidence: 99%

Hydroecological data recovery using artificial intelligence

Nafikova¹,

Aleksandrov²,

Shaniyazova³

et al. 2023

E3S Web of Conf.

View full text Add to dashboard Cite

Artificial neural networks and genetic algorithms have been tested for the restoration of missed hydroecological indicators (hydrological water quality parameters and bottom sediment quality parameters). Algorithms have been developed for recovering missing hydroecological data in the presence and absence of observation data from points upstream and downstream. Neural network models for the restoration of water quality indicators and bottom sediments were tested on the example of the catchment basin of the Bayda and Kidysh rivers in the territory of the Republic of Bashkortostan.

show abstract

Section: Introductionmentioning

confidence: 99%