FORECASTING OF FLOW QUALITATIVE AND QUANTITATIVE PARAMETERS IN KARUN RIVER (MOLASANI-FARSIAT REACH) USING ARTIFICIAL NEURAL NETWORKS

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FATHIAN HOSSEN; HORMOZINEZHAD IMAN

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Journal: JOURNAL OF WETLAND ECOBIOLOGY Year:2011 | Volume:2 | Issue:8 Page(s): 29-43

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Title

FORECASTING OF FLOW QUALITATIVE AND QUANTITATIVE PARAMETERS IN KARUN RIVER (MOLASANI-FARSIAT REACH) USING ARTIFICIAL NEURAL NETWORKS

Author(s)

FATHIAN HOSSEN | HORMOZINEZHAD IMAN | Issue Writer Certificate

Keywords

FLOW QUALITY AND QUANTITYQ4

ARTIFICIAL NEURAL NETWORKSQ2

GENETIC ALGORITHMQ2

SENSITIVITY ANALYSISQ3

KARUN RIVERQ3

Abstract

Estimation and forecasting of the river flow qualitative and quantitative parameters for the administrative decision is considered as one of the objectives of water resource managers and planners. A qualitative and quantitative estimate of river flow by using mathematical models usually is associated with the relatively significant error because of the complexity of mechanisms and Multiplicity of factors affecting the quality.The new technique using ARTIFICIAL NEURAL NETWORKS based Artificial Intelligence (AI) is widely used in various scientific fields, particularly water engineering. In this study, the qualitative and quantitative parameters of KARUN RIVER flow, Molasani-Farsiat reach, including flow discharge, stage, total dissolved solids (TDS) and electrical conductivity (EC) are forecasted up 3days later by using Multi-Layer Perceptron (MLP), Feed Forward (FF) and Radial Basis Function (RBF) of ARTIFICIAL NEURAL NETWORKS. Data period was from 01/01/1369 to 12/07/1378 and 10434 numbers of patterns were achieved after the time delay. The 70%, 20% and 10% of patterns used for training, cross-validation and test of models respectively. The GENETIC ALGORITHM method was used for determining efficient input variables and optimum numbers of neurons in hidden layers of models. Results shows that the precision of FF, MLP and RBF models for estimation and forecasting of qualitative and quantitative parameters of KARUN RIVER flow are 90.6%, 80.5% and 86% respectively. The SENSITIVITY ANALYSIS of output variables to input variables shows that the times of river flow according to month and longitudinal distance from each station to upstream station have significant impact on qualitative and quantitative parameters of river flow respectively.

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APA: Copy

FATHIAN, HOSSEN, & HORMOZINEZHAD, IMAN. (2011). FORECASTING OF FLOW QUALITATIVE AND QUANTITATIVE PARAMETERS IN KARUN RIVER (MOLASANI-FARSIAT REACH) USING ARTIFICIAL NEURAL NETWORKS. JOURNAL OF WETLAND ECOBIOLOGY, 2(8), 29-43. SID. https://sid.ir/paper/174889/en

Vancouver: Copy

FATHIAN HOSSEN, HORMOZINEZHAD IMAN. FORECASTING OF FLOW QUALITATIVE AND QUANTITATIVE PARAMETERS IN KARUN RIVER (MOLASANI-FARSIAT REACH) USING ARTIFICIAL NEURAL NETWORKS. JOURNAL OF WETLAND ECOBIOLOGY[Internet]. 2011;2(8):29-43. Available from: https://sid.ir/paper/174889/en

IEEE: Copy

HOSSEN FATHIAN, and IMAN HORMOZINEZHAD, “FORECASTING OF FLOW QUALITATIVE AND QUANTITATIVE PARAMETERS IN KARUN RIVER (MOLASANI-FARSIAT REACH) USING ARTIFICIAL NEURAL NETWORKS,” JOURNAL OF WETLAND ECOBIOLOGY, vol. 2, no. 8, pp. 29–43, 2011, [Online]. Available: https://sid.ir/paper/174889/en

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