Quality control of daily rainfall data with neural networks

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Title:Main Title: Quality control of daily rainfall data with neural networks
Description:Abstract: A procedure for quality control of daily rainfall, designed to automatically detect erroneous data to be submitted for further manual controls, is herein described. Quality control of daily rainfall data is based on confidence intervals derived by means of neural networks on the basis of contemporaneous data observed at reference stations, since the presence of zero values in the series and the strong variability of precipitation at daily time scale do not allow reliable confidence intervals to be estimated from historical data from the same station. Application of the proposed procedure to automatic stations in Sicily (Italy), enables validation of more than 80% of the data. The accuracy of the procedures is verified by introducing known errors into the available datasets, supposed as correct, and by computing the probabilities of correctly classifying data as validated or not validated.
Identifier:10.1016/j.jhydrol.2008.10.008 (DOI)
Responsible Party
Creators:Guido Sciuto (Author), Brunella Bonaccorso (Author), Antonino Cancelliere (Author), Giuseppe Rossi (Author)
Publisher:Elsevier B.V
Publication Year:2013
Topic
TR32 Topic:Atmosphere
Related Subproject:C1
Subjects:Keywords: Rainfall Data, Quality Control, Neural Networks
File Details
Filename:2009_Scuito_JoH.pdf
Data Type:Text - Article
Size:10 Pages
File Size:834 KB
Date:Accepted: 09.10.2008
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:Completed
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Download Permission:Only Project Members
General Access and Use Conditions:For internal use only
Access Limitations:For internal use only
Licence:[TR32DB] Data policy agreement
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Publication Status:Published
Review Status:Peer reviewed
Publication Type:Article
Article Type:Journal
Source:Journal of Hydrology
Source Website:www.elsevier.com/locate/jhydrol
Volume:364
Number of Pages:10 (13 - 22)
Metadata Details
Metadata Creator:Guido Sciuto
Metadata Created:02.12.2013
Metadata Last Updated:02.12.2013
Subproject:C1
Funding Phase:1
Metadata Language:English
Metadata Version:V50
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