TY - JOUR
T1 - A fusion-based neural network methodology for monthly reservoir inflow prediction using MODIS products
AU - Ghazali, Mahboubeh
AU - Honar, Tooraj
AU - Nikoo, Mohammad Reza
N1 - Publisher Copyright:
© 2019, © 2019 IAHS.
PY - 2018/12/10
Y1 - 2018/12/10
N2 - A model fusion approach was developed based on five artificial neural networks (ANNs) and MODIS products. Static and dynamic ANNs–the multi-layer perceptron (MLP) with one and two hidden layers, general regression neural network (GRNN), radial basis function (RBF) and nonlinear autoregressive network with exogenous inputs (NARX)–were used to predict the monthly reservoir inflow in Mollasadra Dam, Fars Province, Iran. Leaf area index and snow cover from MODIS, and rainfall and runoff data were used to identify eight different combinations to train the models. Statistical error indices and the Borda count method were used to verify and rank the identified combinations. The best results for individual ANNs were combined with MODIS products in a fusion model. The results show that using MODIS products increased the accuracy of predictions, with the MLP with two hidden layers giving the best performance. Also, the fusion model was found to be superior to the best individual ANNs.
AB - A model fusion approach was developed based on five artificial neural networks (ANNs) and MODIS products. Static and dynamic ANNs–the multi-layer perceptron (MLP) with one and two hidden layers, general regression neural network (GRNN), radial basis function (RBF) and nonlinear autoregressive network with exogenous inputs (NARX)–were used to predict the monthly reservoir inflow in Mollasadra Dam, Fars Province, Iran. Leaf area index and snow cover from MODIS, and rainfall and runoff data were used to identify eight different combinations to train the models. Statistical error indices and the Borda count method were used to verify and rank the identified combinations. The best results for individual ANNs were combined with MODIS products in a fusion model. The results show that using MODIS products increased the accuracy of predictions, with the MLP with two hidden layers giving the best performance. Also, the fusion model was found to be superior to the best individual ANNs.
KW - artificial neural network
KW - Borda count method
KW - leaf area index (LAI)
KW - Model fusion
KW - MODIS
KW - monthly reservoir runoff
KW - snow cover
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U2 - 10.1080/02626667.2018.1558365
DO - 10.1080/02626667.2018.1558365
M3 - Article
AN - SCOPUS:85061083045
SN - 0262-6667
VL - 63
SP - 2076
EP - 2096
JO - Hydrological Sciences Journal
JF - Hydrological Sciences Journal
IS - 15-16
ER -