An expert system for predicting longitudinal dispersion coefficient in natural streams by using ANFIS

Riahi-Madvar, Hossien; Ayyoubzadeh, Seyed Ali; Khadangi, Ehsan; Ebadzadeh, Mohammad Mehdi

doi:10.1016/j.eswa.2008.10.043

Cited by 93 publications

(42 citation statements)

References 20 publications

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“…They observed that the performance of their relationship is better than the relationships proposed by Seo & Cheong (1998), Deng et al (2001), Fischer (1975 and Kashefipour & Falconer (2002). Riahi-Madvar et al (2009) developed a new flexible tool to predict the longitudinal dispersion coefficient using adaptive neuro-fuzzy inference system (ANFIS). They found that dispersion coefficient values predicted by the ANFIS model satisfactorily compared with the observed values and also provides better prediction than the relationships proposed by Elder (1959), Fischer (1967, Liu (1977), Seo & Cheong (1998), Koussis & Rodriguez-Mirasol (1998), Deng et al (2001) and Kashefipour & Falconer (2002).…”

Section: Considering Velocity Profile and Vertical Turbulent Diffusionmentioning

confidence: 94%

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Prediction of longitudinal dispersion coefficient using laboratory and field data: relationship comparisons

Ahmad

2012

Hydrology Research

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Knowledge of dispersion of pollutants in streams is necessary for the determination of both the acceptable limits of effluent input and the concentration along the river course. In the far-field, the primary variation of concentration is in one direction and termed longitudinal dispersion; it is independent of the geometrical configuration and type of source. The longitudinal dispersion coefficient represents the dispersive characteristics of a stream and is required to compute the pollutant concentration at downstream locations of the streams. The longitudinal dispersion coefficient can be estimated either from the pollutant concentration profile, stream velocity profile or channel and flow parameters. Many laboratory and field studies have been carried out by several investigators to develop relationships for the longitudinal dispersion coefficient in terms of the known hydraulic characteristics of the stream. This paper evaluates the accuracy of the existing empirical relationships for the prediction of longitudinal dispersion coefficient, using a large volume of data that cover a wide range of flow and channel parameters.

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Section: Considering Velocity Profile and Vertical Turbulent Diffusionmentioning

confidence: 94%

“…If Y is the observed value and Y is the corresponding predicted value, the different performance indices may be defined (Maier & Dandy 1996;Rajurkar et al 2004;Riahi-Madvar et al 2009):…”

Section: Coiviparative Analysismentioning

confidence: 99%

Prediction of longitudinal dispersion coefficient using laboratory and field data: relationship comparisons

Ahmad

2012

Hydrology Research

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“…Although the Anfis approach is applied for different hydrological processes, this method is still rarely mentioned in hydraulic research (Kocabas and Ulker, 2006;Riahi-Madvar et al, 2009;Yang and Chang, 2005). Emiroglu et al (2010) used the Anfis method for predicting the discharge coefficient of a triangular labyrinth side weir located on the straight channel.…”

Section: Introductionmentioning

confidence: 98%

Anfis to estimate discharge capacity of rectangular side weir

Kisi¹,

Bilhan²,

Emiroğlu³

2013

Proceedings of the Institution of Civil Engineers - Water Manag

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The adaptive neuro-fuzzy inference system (Anfis) is considered for flow over rectangular side weirs located on a straight channel as a substantial part of distribution channels in irrigation systems and treatment units. To estimate the outflow over a rectangular sharp-crested side weir, the discharge coefficient in the side weir equation needs to be determined in accordance with the effective dimensionless parameters F1 (Froude number), L/b (weir length/channel width), L/h1 (weir length/flow depth), and p/h1 (weir height/flow depth). The discharge coefficient of rectangular side weirs was determined using 843 laboratory test results. The performance of the Anfis model is compared with multilinear and nonlinear regression models. The criteria used for the evaluation of the performance of models are root mean square errors (RMSE), mean absolute errors (MAE) and correlation coefficient (R) statistics. Comparison results indicated that the Anfis technique could be successfully employed in modelling discharge coefficients. It is found that the Anfis model with RMSE of 0·043 in the test period is superior in estimation of discharge coefficient than the multiple nonlinear and linear regression models with RMSE of 0·054 and 0·106, respectively.

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“…Therefore, the hybrid of fuzzy systems based on logical rules, and artificial neural networks which are able to extract knowledge from numerical information, enables us to use the available information to develop a model in addition to benefiting from the human knowledge. Thus, the resultant method is an adaptive neuro-fuzzy inference technique (Riahi-Madvar et al, 2009;Ebrat & Ghodis, 2014). According to Insurance Bylaw 69 (approved by the High Insurance Council), policyholder risk classifications of International Actuarial Association (IAA), the requirements of International Associations of Insurance Supervisors (IAIS), statistical and modeling constraints on Iranian commercial insurance industry, the risk of a commercial insurance company consists of the following items in Iranian solvency model: insurance or underwriting risk, market risk, credit risk, and liquidity risk (Shahriar, 2014).…”

Section: Introductionmentioning

confidence: 99%

Business risk evaluation and management of Iranian commercial insurance companies

Hessami¹

2018

10.5267/j.msl

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Nowadays, with the expansion of economic businesses and also the dependency of economic activists on the insurance industry to provide the capitals security, there is now a growing need to identify and evaluate risks of the insurance industry. Therefore, in this study, a comprehensive model was developed to evaluate and manage business risk by reviewing the research literature, extensively. For this purpose, an adaptive neuro-fuzzy inference system (ANFIS) was developed for every business risk after identifying the relevant dimensions and the criteria and collecting the necessary data from the central insurance databases. Finally, a general model was presented to evaluate and manage risks of the insurance industry. Four major problems were also considered: optimal and efficient normalization, optimal training for testing ratio for every neural network, model validation, and the easiness of user communication with the system. The results show that the model can provide an accurate estimation for risk evaluation and management. Thus, this system can be considered as an appropriate tool for business risk evaluation and management of insurance companies. Furthermore, the effectiveness of this method in evaluating and managing the risk at insurance companies can be turned into a neural network and such a neural network can be used as an appropriate decision-making support tool.

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An expert system for predicting longitudinal dispersion coefficient in natural streams by using ANFIS

Cited by 93 publications

References 20 publications

Prediction of longitudinal dispersion coefficient using laboratory and field data: relationship comparisons

Prediction of longitudinal dispersion coefficient using laboratory and field data: relationship comparisons

Anfis to estimate discharge capacity of rectangular side weir

Business risk evaluation and management of Iranian commercial insurance companies

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