Nkiaka, E, Nawaz, NR and Lovett, JC (2016) Using Self-Organizing Maps to infill missing data in hydro-meteorological time series from the Logone catchment, Lake Chad basin. Environmental Monitoring and Assessment, 188. 400. ISSN 0167-6369
Abstract
Hydro-meteorological data is an important asset that can enhance management of water resources. But existing data often contains gaps, leading to uncertainties and so compromising their use. Although many methods exist for infilling data gaps in hydro-meteorological time series, many of these methods require inputs from neighbouring stations, which are often not available, while other methods are computationally demanding. Computing techniques such Artificial Intelligence can be used to address this challenge. Self-Organizing Maps (SOMs), which are a type of Artificial Neural Network, was used for infilling gaps in a hydro-meteorological time series in a Sudano-Sahel catchment. The coefficients of determination obtained were all above 0.75 and 0.65 while the average topographic error was 0.008 and 0.02 for rainfall and river discharge time series respectively. These results further indicate that SOMs are a robust and efficient method for infilling missing gaps in hydro-meteorological time series.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2016, Springer International Publishing Switzerland. This is an author produced version of a paper published in Environmental Monitoring and Assessment. Uploaded in accordance with the publisher's self-archiving policy. The final publication is available at Springer via: http://dx.doi.org/10.1007/s10661-016-5385-1 |
Keywords: | Artificial Neural Networks, hydro-meteorological data, infilling missing data, Logone catchment, Self-Organizing Maps |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Jun 2016 11:16 |
Last Modified: | 19 Jul 2017 00:17 |
Published Version: | http://dx.doi.org/10.1007/s10661-016-5385-1 |
Status: | Published |
Publisher: | Springer Verlag (Germany) |
Identification Number: | 10.1007/s10661-016-5385-1 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:100773 |