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Nearest‐neighbor methods for nonparametric rainfall‐runoff forecasting
Author(s) -
Karlsson M.,
Yakowitz S.
Publication year - 1987
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/wr023i007p01300
Subject(s) - hydrograph , autoregressive model , nonparametric statistics , series (stratigraphy) , surface runoff , nonparametric regression , convergence (economics) , sample (material) , mathematics , computer science , statistics , econometrics , geology , ecology , paleontology , chemistry , chromatography , economic growth , economics , biology
This paper introduces a nonparametric regression method for time series to the stochastic hydrology literature. The technique, known as the nearest‐neighbor (NN) method, is motivated by the classical rainfall‐runoff forecasting problem. We have shown in a companion paper (Yakowitz, 1987) that in the time series setting it has attractive large‐sample properties. As summarized herein, these include asymptotic convergence to the optimal forecaster under many circumstances in which the forecaster is not a linear function of the data. The technique is applied to a sequence of about 3000 seasonalized rainfall‐runoff pairs from the Coshocton Watershed. We compare the NN forecasting performance on a split sample with those of autoregressive maximum and instantaneous unit hydrograph forecasters.