COMPARISON OF DAILY SUSPENDED SEDIMENT LOAD ESTIMATIONS BY SEDIMENT RATING CURVE AND NEURAL NETWORK MODELS (CASE STUDY: GHAZAGHLI STATION IN GOLESTAN PROVINCE)

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DEHGHANI N.; VAFAKHAH M.

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Journal: Journal of Water and Soil Conservation Year:2013 | Volume:20 | Issue:2 Page(s): 221-230

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Title

COMPARISON OF DAILY SUSPENDED SEDIMENT LOAD ESTIMATIONS BY SEDIMENT RATING CURVE AND NEURAL NETWORK MODELS (CASE STUDY: GHAZAGHLI STATION IN GOLESTAN PROVINCE)

Author(s)

DEHGHANI N. | VAFAKHAH M. | Issue Writer Certificate

Keywords

SEDIMENT RATING CURVEQ2

ARTIFICIAL NEURAL NETWORKSQ2

DAILY SUSPENDED SEDIMENT LOADQ2

GHAZAGHLI STATIONQ2

GORGANROUD WATERSHEDQ2

Abstract

The accurate estimation of sediments transported by rivers is very important in many water resource management projects. Due to nonlinear behavior of hydrologic variables, application of classic methods e.g. SEDIMENT RATING CURVE (SRC) does not have adequate precision. Therefore, intelligent methods can be applied as an efficient tool in hydrologic parameters modeling. In this study, ARTIFICIAL NEURAL NETWORKS (ANNs) such as multilayer perceptron (MLP) and radial basis function (RBF) and different SRC methods including annually, hydrologic similar, high and low flows, clusters average limit, classification of discharges, hydrograph condition and seasonal classification were carried out for DAILY SUSPENDED SEDIMENT LOAD estimation in GHAZAGHLI STATION, located in GORGANROUD WATERSHED. For this reason, the measured DAILY SUSPENDED SEDIMENT LOAD data during the period of 1982 to 1985 were used. Three years of data were used for training sets and 1 year for testing sets. The results show that, the classic discharge method and MLP which is used current streamflow, antecedent streamflow and two days of antecedent streamflow as input parameters are the best models among the various selected models. The results also show that the accuracy of neural networks methods is more than the SRC methods.

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APA: Copy

DEHGHANI, N., & VAFAKHAH, M.. (2013). COMPARISON OF DAILY SUSPENDED SEDIMENT LOAD ESTIMATIONS BY SEDIMENT RATING CURVE AND NEURAL NETWORK MODELS (CASE STUDY: GHAZAGHLI STATION IN GOLESTAN PROVINCE). JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), 20(2), 221-230. SID. https://sid.ir/paper/156545/en

Vancouver: Copy

DEHGHANI N., VAFAKHAH M.. COMPARISON OF DAILY SUSPENDED SEDIMENT LOAD ESTIMATIONS BY SEDIMENT RATING CURVE AND NEURAL NETWORK MODELS (CASE STUDY: GHAZAGHLI STATION IN GOLESTAN PROVINCE). JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES)[Internet]. 2013;20(2):221-230. Available from: https://sid.ir/paper/156545/en

IEEE: Copy

N. DEHGHANI, and M. VAFAKHAH, “COMPARISON OF DAILY SUSPENDED SEDIMENT LOAD ESTIMATIONS BY SEDIMENT RATING CURVE AND NEURAL NETWORK MODELS (CASE STUDY: GHAZAGHLI STATION IN GOLESTAN PROVINCE),” JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), vol. 20, no. 2, pp. 221–230, 2013, [Online]. Available: https://sid.ir/paper/156545/en

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