Using self-organizing maps to infill missing data in hydro-meteorological time series from the Logone catchment, Lake Chad basin

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• 作者：E. Nkiaka ; N. R. Nawaz ; J. C. Lovett
• 刊名：Environmental Monitoring and Assessment
• 出版年：2016
• 出版时间：July 2016
• 年：2016
• 卷：188
• 期：7
• 全文大小：1,660 KB
• 刊物类别：Earth and Environmental Science
• 刊物主题：Environment
  Monitoring, Environmental Analysis and Environmental Ecotoxicology
  Ecology
  Atmospheric Protection, Air Quality Control and Air Pollution
  Environmental Management
  
• 出版者：Springer Netherlands
• ISSN：1573-2959
• 卷排序：188

文摘

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 as artificial intelligence can be used to address this challenge. Self-organizing maps (SOMs), which are a type of artificial neural network, were 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.KeywordsArtificial neural networksHydro-meteorological dataInfilling missing dataLogone catchmentSelf-organizing maps