Literature DB >> 26812952

Flow monitoring with a camera: a case study on a flood event in the Tiber River.

F Tauro1, G Olivieri1, A Petroselli2, M Porfiri3, S Grimaldi4,5.   

Abstract

Monitoring surface water velocity during flood events is a challenging task. Techniques based on deploying instruments in the flow are often unfeasible due to high velocity and abundant sediment transport. A low-cost and versatile technology that provides continuous and automatic observations is still not available. Among remote methods, large-scale particle image velocimetry (LSPIV) is an optical method that computes surface water velocity maps from videos recorded with a camera. Here, we implement and critically analyze findings obtained from a recently introduced LSPIV experimental configuration during a flood event in the Tiber River at a cross section located in the center of Rome, Italy. We discuss the potential of LSPIV observations in challenging environmental conditions by presenting results from three tests performed during the hydrograph flood peak and recession limb of the event for different illumination and weather conditions. The obtained surface velocity maps are compared to the rating curve velocity and to benchmark velocity values. Experimental findings show that optical methods should be preferred in extreme conditions. However, their practical implementation may be associated with further hurdles and uncertainties.

Entities:  

Keywords:  Camera observations; Flow measurement; Flow monitoring; Flow velocity; Large-scale particle image velocimetry; Surface flow

Mesh:

Year:  2016        PMID: 26812952     DOI: 10.1007/s10661-015-5082-5

Source DB:  PubMed          Journal:  Environ Monit Assess        ISSN: 0167-6369            Impact factor:   2.513


  3 in total

1.  Image quality assessment: from error visibility to structural similarity.

Authors:  Zhou Wang; Alan Conrad Bovik; Hamid Rahim Sheikh; Eero P Simoncelli
Journal:  IEEE Trans Image Process       Date:  2004-04       Impact factor: 10.856

2.  A photonic wall pressure sensor for fluid mechanics applications.

Authors:  M Manzo; T Ioppolo; U K Ayaz; V Lapenna; M V Ötügen
Journal:  Rev Sci Instrum       Date:  2012-10       Impact factor: 1.523

3.  Unraveling flow patterns through nonlinear manifold learning.

Authors:  Flavia Tauro; Salvatore Grimaldi; Maurizio Porfiri
Journal:  PLoS One       Date:  2014-03-10       Impact factor: 3.240

  3 in total
  1 in total

1.  Proposal of a Method to Determine the Correlation between Total Suspended Solids and Dissolved Organic Matter in Water Bodies from Spectral Imaging and Artificial Neural Networks.

Authors:  Maurício R Veronez; Lucas S Kupssinskü; Tainá T Guimarães; Emilie C Koste; Juarez M da Silva; Laís V de Souza; William F M Oliverio; Rogélio S Jardim; Ismael É Koch; Jonas G de Souza; Luiz Gonzaga; Frederico F Mauad; Leonardo C Inocencio; Fabiane Bordin
Journal:  Sensors (Basel)       Date:  2018-01-09       Impact factor: 3.576

  1 in total

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