PREDICTION OF LONGITUDINAL DISPERSION COEFFICIENT IN NATURAL RIVER USING GMDH DATA DRIVEN APPROACH

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QADERI K.; HOSEINZADEH M.

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Journal: Iranian Journal of Irrigation and Drainage Year:2016 | Volume:10 | Issue:5 Page(s): 581-593

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Title

PREDICTION OF LONGITUDINAL DISPERSION COEFFICIENT IN NATURAL RIVER USING GMDH DATA DRIVEN APPROACH

Author(s)

QADERI K. | HOSEINZADEH M. | Issue Writer Certificate

Keywords

DATA DRIVENQ1

DISPERSION COEFFICIENT METHODQ1

EMPIRICAL RELATIONQ1

ENVIRONMENTALQ1

GMDHQ2

MODELINGQ1

Abstract

Accurate estimate of longitudinal dispersion coefficient is important in many hydraulic and ENVIRONMENTAL problems in rivers such as river engineering, intake designs, MODELING flow in estuaries and risk assessments of pollutants into river flows. To accurate investigation of water quality using one dimensional model, the precise estimation of longitudinal dispersion coefficient is required. Direct measurements of longitudinal dispersion coefficients, with the aid of concentration samples taken in upstream and downstream of rivers is rather seldom.Recent research works indicate that, using the DATA DRIVEN method can improve the precise estimation of longitudinal dispersion coefficient in natural rivers. In this research, the usefulness and performance of Group Method of Data Handling (GMDH) approach are examined for predicting longitudinal dispersion coefficient in natural channels. A set of 71 data sets from different river has been gathered so that 51 sets of whole data were used for training and 20remaing sets were used for test data sets. The hydraulic and geometric variables such as mean flow depth (H), width of channel (W), mean flow velocity (U), channel sinuosity (s) and shear velocity (U*) are used as input variables to predict longitudinal dispersion coefficient (Kx). A computer program based on GMDH approach is written in MATLAB software for Kx MODELING. Based on the values of various performance indices, R2, RMSE, CC and DR, it is concluded that GMDH model in both training and validation period predicts the longitudinal dispersion coefficient more accurately. Comparison of GMDH model with empirical approach and another DATA DRIVEN method such as ANN, SVM and GA confirm that GMDH shows remarkably good performance in capturing governing pattern in longitudinal dispersion phenomena in natural rivers. Hence GMDH can be used as an efficient computational paradigm in the estimation of longitudinal dispersion coefficient in natural channel.

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APA: Copy

QADERI, K., & HOSEINZADEH, M.. (2016). PREDICTION OF LONGITUDINAL DISPERSION COEFFICIENT IN NATURAL RIVER USING GMDH DATA DRIVEN APPROACH. IRANIAN JOURNAL OF IRRIGATION AND DRAINAGE, 10(5 ), 581-593. SID. https://sid.ir/paper/131594/en

Vancouver: Copy

QADERI K., HOSEINZADEH M.. PREDICTION OF LONGITUDINAL DISPERSION COEFFICIENT IN NATURAL RIVER USING GMDH DATA DRIVEN APPROACH. IRANIAN JOURNAL OF IRRIGATION AND DRAINAGE[Internet]. 2016;10(5 ):581-593. Available from: https://sid.ir/paper/131594/en

IEEE: Copy

K. QADERI, and M. HOSEINZADEH, “PREDICTION OF LONGITUDINAL DISPERSION COEFFICIENT IN NATURAL RIVER USING GMDH DATA DRIVEN APPROACH,” IRANIAN JOURNAL OF IRRIGATION AND DRAINAGE, vol. 10, no. 5 , pp. 581–593, 2016, [Online]. Available: https://sid.ir/paper/131594/en

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