Accuracy and Uncertainty Analysis of Intelligent Techniques for Predicting the Longitudinal Dispersion Coefficient in Rivers

Autor:	Abbas Akbarzadeh, Roohollah Noori, Ashkan Farokhnia, Amir Khakpour, Mohammad Salman Sabahi
Jazyk:	English<br />Persian
Rok vydání:	2010
Předmět:	Monte-Carlo Longitudinal dispersion coefficient neural network adaptive neuro-fuzzy inference system Bound Wide Factor Technology Water supply for domestic and industrial purposes TD201-500 Sewage collection and disposal systems. Sewerage TD511-780
Zdroj:	آب و فاضلاب, Vol 21, Iss 3, Pp 99-107 (2010)
Druh dokumentu:	article
ISSN:	1024-5936 2383-0905
Popis:	Accurate prediction of longitudinal dispersion coefficient (LDC) can be useful for the determination of pollutants concentration distribution in natural rivers. However, the uncertainty associated with the results obtained from forecasting models has a negative effect on pollutant management in water resources. In this research, appropriate models are first developed using ANN and ANFIS techniques to predict the LDC in natural streams. Then, an uncertainty analysis is performed for ANN and ANFIS models based on Monte-Carlo simulation. The input parameters of the models are related to hydraulic variables and stream geometry. Results indicate that ANN is a suitable model for predicting the LDC, but it is also associated with a high level of uncertainty. However, results of uncertainty analysis show that ANFIS model has less uncertainty; i.e. it is the best model for forecasting satisfactorily the LDC in natural streams.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/bd44800f428048f69740d0386f47e9ad Zobrazit plný text záznamu View record in DOAJ