TY - JOUR
T1 - Application of short-term water demand prediction model to Seoul
AU - Joo, C. N.
AU - Koo, J. Y.
AU - Yu, M. J.
PY - 2002
Y1 - 2002
N2 - To predict daily water demand for Seoul, Korea, the artificial neural network (ANN) was used. For the cross correlation, the factors affecting water demand such as maximum temperature, humidity, and wind speed as natural factors, holidays as a social factor and daily demand 1 day before were used. From the results of learning using various hidden layers and units in order to establish the structure of optimal ANN, the case of 3 hidden layers and numbers of unit with the same number of input factors showed the best result and, therefore, it was applied to seasonal water demand prediction. The performance of ANN was compared with a multiple regression method. We discuss the representation ability of the model building process and the applicability of the ANN approach for the daily water demand prediction. ANN provided reasonable results for time series prediction.
AB - To predict daily water demand for Seoul, Korea, the artificial neural network (ANN) was used. For the cross correlation, the factors affecting water demand such as maximum temperature, humidity, and wind speed as natural factors, holidays as a social factor and daily demand 1 day before were used. From the results of learning using various hidden layers and units in order to establish the structure of optimal ANN, the case of 3 hidden layers and numbers of unit with the same number of input factors showed the best result and, therefore, it was applied to seasonal water demand prediction. The performance of ANN was compared with a multiple regression method. We discuss the representation ability of the model building process and the applicability of the ANN approach for the daily water demand prediction. ANN provided reasonable results for time series prediction.
KW - Artificial neural network
KW - Cross correlation
KW - Multiple regression
KW - Short-term water demand
UR - http://www.scopus.com/inward/record.url?scp=0036384659&partnerID=8YFLogxK
U2 - 10.2166/wst.2002.0687
DO - 10.2166/wst.2002.0687
M3 - Article
C2 - 12380999
AN - SCOPUS:0036384659
SN - 0273-1223
VL - 46
SP - 255
EP - 261
JO - Water Science and Technology
JF - Water Science and Technology
IS - 6-7
ER -