COMPARISON OF AUTOREGRESSIVE STATIC AND ARTIFICIAL DYNAMIC NEURAL NETWORK FOR THE FORECASTING OF MONTHLY INFLOW OF DEZ RESERVOIR

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BANIHABIB MOHAMMAD EBRAHIM; VALIPOOR MOHAMMAD; MAHMOOD BEHBAHANI S.

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Journal: JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY Year:2012 | Volume:13 | Issue:4 (51) Page(s): 1-14

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Information Journal Paper

Title

COMPARISON OF AUTOREGRESSIVE STATIC AND ARTIFICIAL DYNAMIC NEURAL NETWORK FOR THE FORECASTING OF MONTHLY INFLOW OF DEZ RESERVOIR

Author(s)

BANIHABIB MOHAMMAD EBRAHIM | VALIPOOR MOHAMMAD | MAHMOOD BEHBAHANI S. | Issue Writer Certificate

Keywords

FORECASTING OF THE RESERVOIR INFLOWQ2

DEZ DAMQ3

AUTOREGRESSIVE ARTIFICIAL NEURAL NETWORK MODELQ2

Abstract

In this paper, the capability of autoregressive static and artificial dynamic neural networks models was compared for forecasting of monthly inflow of Dez reservoir. In previous researches, static and artificial dynamic neural networks models have not been compared for above-mentioned propose. In addition, using artificial neural network model as an autoregressive model is innovation point of this research. Monthly flow data of Dez station in Dez River in years1955 to 2001 is used in this research. Data of 42 former years and 5 recent years are used for Training and testing data set, respectively. Different structure for the static and artificial dynamic neural network models were evaluated by comparing the root-mean-square error (RMSE) of the models. First, static and artificial dynamic neural network models were selected in training phase using data from October 1955 to September 1997. Then, using the selected structures, the monthly forecasted inflow of reservoir was compared with observed data from October 1997 to September 2001. Also, two types of radial and sigmoid activation function and the number of neurons in the hidden layer were investigated in this study. Results showed that the best model to forecast the reservoir inflow is AUTOREGRESSIVE ARTIFICIAL NEURAL NETWORK MODEL associated with the sigmoid activation function and 17 neurons in the hidden layers. Artificial dynamic neural network model with sigmoid activation function can forecast reservoir inflow for 5 years better than static artificial neural network's model.

Cites

AUTOREGRESSIVE SIMULATION OF ZARRINEHRUD RIVER BASIN RUNOFF USING PROCRUSTES ANALYSIS METHOD AND ARTIFICIAL NEURAL NETWORK AND SUPPORT VECTOR MACHINE MODELS

References

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APA: Copy

BANIHABIB, MOHAMMAD EBRAHIM, VALIPOOR, MOHAMMAD, & MAHMOOD BEHBAHANI, S.. (2012). COMPARISON OF AUTOREGRESSIVE STATIC AND ARTIFICIAL DYNAMIC NEURAL NETWORK FOR THE FORECASTING OF MONTHLY INFLOW OF DEZ RESERVOIR. JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY, 13(4 (51)), 1-14. SID. https://sid.ir/paper/87401/en

Vancouver: Copy

BANIHABIB MOHAMMAD EBRAHIM, VALIPOOR MOHAMMAD, MAHMOOD BEHBAHANI S.. COMPARISON OF AUTOREGRESSIVE STATIC AND ARTIFICIAL DYNAMIC NEURAL NETWORK FOR THE FORECASTING OF MONTHLY INFLOW OF DEZ RESERVOIR. JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY[Internet]. 2012;13(4 (51)):1-14. Available from: https://sid.ir/paper/87401/en

IEEE: Copy

MOHAMMAD EBRAHIM BANIHABIB, MOHAMMAD VALIPOOR, and S. MAHMOOD BEHBAHANI, “COMPARISON OF AUTOREGRESSIVE STATIC AND ARTIFICIAL DYNAMIC NEURAL NETWORK FOR THE FORECASTING OF MONTHLY INFLOW OF DEZ RESERVOIR,” JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY, vol. 13, no. 4 (51), pp. 1–14, 2012, [Online]. Available: https://sid.ir/paper/87401/en

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