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USING ARTIFICIAL NEURAL NETWORKS TO ESTIMATE MISSING RAINFALL DATA 1
Author(s) -
Kuligowski Robert J.,
Barros Ana P
Publication year - 1998
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.1998.tb05443.x
Subject(s) - missing data , artificial neural network , interpolation (computer graphics) , backpropagation , series (stratigraphy) , computer science , data mining , time series , sample (material) , algorithm , artificial intelligence , machine learning , geology , motion (physics) , paleontology , chemistry , chromatography
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 and 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.