Application of Artificial Neural Networks to Rainfall Forecasting in the Geum River Basin, Korea

This study develops a late spring-early summer rainfall forecasting model using an artificial neural network (ANN) for the Geum River Basin in South Korea. After identifying the lagged correlation between climate indices and the rainfall amount in May and June, 11 significant input variables were se...

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Bibliographic Details
Published in:Water
Main Authors: Jeongwoo Lee, Chul-Gyum Kim, Jeong Eun Lee, Nam Won Kim, Hyeonjun Kim
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2018
Subjects:
artificial neural network
rainfall forecasting
climate indices
relative importance of input variables
Hydraulic engineering
TC1-978
Water supply for domestic and industrial purposes
TD201-500
Pacific
North Atlantic
North Atlantic oscillation
Online Access:https://doi.org/10.3390/w10101448
https://doaj.org/article/36c5bc490b8245c28bc83c01c66ca36d
Description
Summary:This study develops a late spring-early summer rainfall forecasting model using an artificial neural network (ANN) for the Geum River Basin in South Korea. After identifying the lagged correlation between climate indices and the rainfall amount in May and June, 11 significant input variables were selected for the preliminary ANN structure. From quantification of the relative importance of the input variables, the lagged climate indices of East Atlantic Pattern (EA), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), East Pacific/North Pacific Oscillation (EP/NP), and Tropical Northern Atlantic Index (TNA) were identified as significant predictors and were used to construct a much simpler ANN model. The final best ANN model, with five input variables, showed acceptable performance with relative root mean square errors of 25.84%, 32.72%, and 34.75% for training, validation, and testing data sets, respectively. The hit score, which is the number of hit years divided by the total number of years, was more than 60%, which indicates that the ANN model successfully predicts rainfall in the study area. The developed ANN model, incorporated with lagged global climate indices, could allow for more timely and flexible management of water resources and better preparation against potential droughts in the study region.

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