TY - JOUR
T1 - Application of NN-ARX model to predict groundwater levels in the Neishaboor Plain, Iran
AU - Izady, Azizallah
AU - Davary, Kamran
AU - Alizadeh, Amin
AU - Nia, A Moghaddam
AU - Ziaei, Ali Naghi
AU - Hasheminia, Seyed Majid
PY - 2013
Y1 - 2013
N2 - There is no doubt that groundwater is an important and vital source of water supply in arid and semi-arid areas. Therefore, prediction of groundwater level fluctuations is necessary for planning conjunctive use in these areas. This research was aimed to predict groundwater levels in the Neishaboor plain using Neural Network - AutoRegressive eXtra input (NN-ARX) and Static-NN models. The NN-ARX model determines a nonlinear ARX model of a dynamic system by training a hidden layer neural network with the Levenberg-Marquardt algorithm. In this model the current outputs depend not only on the current inputs, but also on the inputs and outputs at the pervious time periods. The available observation wells in the study area were clustered according to their fluctuation behavior using the "Ward" method, which resulted in six areal zones. Then, for each cluster, an observation well was selected as its representative, and for each zone, values of monthly precipitation, temperature and groundwater extraction were estimated. The best input of the Static-NN model was identified using combination of Gamma Test and Genetic Algorithm. Also, Gamma Test is applied to identify the length of the training dataset. The results showed that the NN-ARX model was suitable and more practical. The performance indicators (R2 = 0.97, RMSE = 0.03 m, ME = --0.07 m and R 2 = 0.81, RMSE = 0.35 m, ME = 0.60 m, respectively for the best and worst performance of model) reveals the effectiveness of this model. Moreover, these results were compared with the results of a static-NN model using t-test, which showed the superiority of the NN-ARX over the static-NN.
AB - There is no doubt that groundwater is an important and vital source of water supply in arid and semi-arid areas. Therefore, prediction of groundwater level fluctuations is necessary for planning conjunctive use in these areas. This research was aimed to predict groundwater levels in the Neishaboor plain using Neural Network - AutoRegressive eXtra input (NN-ARX) and Static-NN models. The NN-ARX model determines a nonlinear ARX model of a dynamic system by training a hidden layer neural network with the Levenberg-Marquardt algorithm. In this model the current outputs depend not only on the current inputs, but also on the inputs and outputs at the pervious time periods. The available observation wells in the study area were clustered according to their fluctuation behavior using the "Ward" method, which resulted in six areal zones. Then, for each cluster, an observation well was selected as its representative, and for each zone, values of monthly precipitation, temperature and groundwater extraction were estimated. The best input of the Static-NN model was identified using combination of Gamma Test and Genetic Algorithm. Also, Gamma Test is applied to identify the length of the training dataset. The results showed that the NN-ARX model was suitable and more practical. The performance indicators (R2 = 0.97, RMSE = 0.03 m, ME = --0.07 m and R 2 = 0.81, RMSE = 0.35 m, ME = 0.60 m, respectively for the best and worst performance of model) reveals the effectiveness of this model. Moreover, these results were compared with the results of a static-NN model using t-test, which showed the superiority of the NN-ARX over the static-NN.
KW - Gamma test
KW - Genetic algorithm
KW - Groundwater
KW - Iran
KW - NN-ARX model
KW - Ward clustering
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U2 - 10.1007/s11269-013-0432-y
DO - 10.1007/s11269-013-0432-y
M3 - Article
SN - 0920-4741
VL - 27
SP - 4773
EP - 4794
JO - Water Resources Management
JF - Water Resources Management
IS - 14
ER -