Using artificial neural networks to estimate missing rainfall data

R. J. Kuligowski, A. P. Barros

Research output: Contribution to journalArticlepeer-review


Missing rainfall data from a time series or a spatial field of observations can present a serious obstacle to data analysis, modeling studies and operational forecasting in hydrology. Numerous schemes for replacing missing data have been proposed, ranging from simple weighted averages of data points that are nearby in time and space to complex statistically-based interpolation methods arid function fitting schemes. This paper presents a technique for replacing missing spatial data using a backpropagation neural network applied to concurrent data from nearby gauges. Tests performed on a sample of gauges in the Middle Atlantic region of the United States show that this technique produces results that compare favorably to simple techniques such as arithmetic and distance-weighted averages of the values from nearby gauges, and also to linear optimization methods such as regression.

Original languageEnglish (US)
Pages (from-to)1437-1447
Number of pages11
JournalJournal of the American Water Resources Association
Issue number6
StatePublished - 1998
Externally publishedYes


  • Backpropagation neural networks
  • Data estimation
  • Missing data
  • Rainfall

ASJC Scopus subject areas

  • Ecology
  • Water Science and Technology
  • Earth-Surface Processes


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