COMPARISON OF BAYESIAN NEURAL NETWORKS AND ARTIFICIAL NEURAL NETWORK TO ESTIMATE SUSPENDED SEDIMENTS IN THE RIVERS (CASE STUDY: SIMINEH ROOD)

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GHORBANI MOHAMMAD ALI; DEHGHANI REZA

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Journal: JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY Year:2017 | Volume:19 | Issue:2 (73) Page(s): 1-13

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Title

COMPARISON OF BAYESIAN NEURAL NETWORKS AND ARTIFICIAL NEURAL NETWORK TO ESTIMATE SUSPENDED SEDIMENTS IN THE RIVERS (CASE STUDY: SIMINEH ROOD)

Author(s)

GHORBANI MOHAMMAD ALI | DEHGHANI REZA | Issue Writer Certificate

Keywords

DISCHARGEQ1

SIMINEHROODQ1

BAYESIAN NEURAL NETWORKQ1

ARTIFICIAL NEURAL NETWORKQ1

Abstract

Background and Purpose: Simulation and evaluation of sediment are important issues in water resources management. Common methods for measuring sediment concentration are generally time consuming and costly and sometimes does not have enough accuracy.Materials and Methods: In this research, we have tried to evaluate sediment amounts, using BAYESIAN NEURAL NETWORK for Simineh-Rood, West Azerbaijan, Iran, and compare it with common ARTIFICIAL NEURAL NETWORKs. Monthly river DISCHARGE, temperature and total dissolved solids for time period (1354-1383) was used as input and sediment DISCHARGE for output. Criteria of correlation coefficient, root mean square error and Nash Sutcliff bias coefficient were used to evaluate and compare the performance of models.Results: The results showed that three models smart estimate sediment DISCHARGE with acceptable accuracy, but in terms of accuracy, the BAYESIAN NEURAL NETWORK model had the highest correlation coefficient (0.832), minimum root mean square error (0.071 ton/day) and the Nash Sutcliff (0.692) and the bias (0.0001) and hence was chosen the prior in the verification stage.Discussion and conclusions: Finally, the results showed that the BAYESIAN NEURAL NETWORK has great capability in estimating minimum and maximum sediment DISCHARGE values.

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APA: Copy

GHORBANI, MOHAMMAD ALI, & DEHGHANI, REZA. (2017). COMPARISON OF BAYESIAN NEURAL NETWORKS AND ARTIFICIAL NEURAL NETWORK TO ESTIMATE SUSPENDED SEDIMENTS IN THE RIVERS (CASE STUDY: SIMINEH ROOD). JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY, 19(2 (73)), 1-13. SID. https://sid.ir/paper/87082/en

Vancouver: Copy

GHORBANI MOHAMMAD ALI, DEHGHANI REZA. COMPARISON OF BAYESIAN NEURAL NETWORKS AND ARTIFICIAL NEURAL NETWORK TO ESTIMATE SUSPENDED SEDIMENTS IN THE RIVERS (CASE STUDY: SIMINEH ROOD). JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY[Internet]. 2017;19(2 (73)):1-13. Available from: https://sid.ir/paper/87082/en

IEEE: Copy

MOHAMMAD ALI GHORBANI, and REZA DEHGHANI, “COMPARISON OF BAYESIAN NEURAL NETWORKS AND ARTIFICIAL NEURAL NETWORK TO ESTIMATE SUSPENDED SEDIMENTS IN THE RIVERS (CASE STUDY: SIMINEH ROOD),” JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY, vol. 19, no. 2 (73), pp. 1–13, 2017, [Online]. Available: https://sid.ir/paper/87082/en

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