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:: Volume 2, Issue 5 (2-2009) ::
jwmseir 2009, 2(5): 21-30 Back to browse issues page
Streamflow Forecasting Using Neuro-Fuzzy and Time Series Methods
Abolhasan Fathabadi *, Ali Salajegheh, Mohammad Mahdavi
Abstract:   (20597 Views)

Simulation of river flow in order to understand the river yield in the future is one of the important and

practical issues in water resource management. In this study, monthly discharge of Taleghan river in Glinak

stations at one step proceeding were forecasted using Artificial Intelligent (Artificial Neural Network MLP,

ANFIS with Grid Partition and Subtractive Clustering) and time series methods. Two inputs including raw

discharge data and de-seasonalised discharge data were used for different models. For time series models,

ARIMA (3,0,0)(0,1,1) were selected as suitable model. The optimum structure in Artificial Intelligence

method after pre-processing was determined using input and output data based on trial and error, and then,

using the optimum structure, the streamflow discharge was forecasted. After the output of each single

model was obtained, the structure of hybrid models were determined. The results showed hybrid methods

3 and 2 have the best application and time series model has better results than Artificial Intelligent methods.

Keywords: Time Serie, ANFIS, River Discharge, Artifitial Neural Network and Taleghan River.
Full-Text [PDF 299 kb]   (2451 Downloads)    
Type of Study: Research | Subject: Special
Received: 2013/02/2 | Published: 2009/02/15
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Fathabadi A, Salajegheh A, mahdavi M. Streamflow Forecasting Using Neuro-Fuzzy and Time Series Methods. jwmseir. 2009; 2 (5) :21-30
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Volume 2, Issue 5 (2-2009) Back to browse issues page
مجله علوم ومهندسی آبخیزداری ایران Iranian Journal of Watershed Management Science and Engineering
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