Predicting turbulent flow friction coefficient using ANFIS technique

Bardestani, Sara; Givehchi, Mohammad; Younesi, Emran; Sajjadi, Shahin; Shamshirband, Shahaboddin; Petković, Dalibor

doi:10.1007/s11760-016-0948-8

Cited by 21 publications

(12 citation statements)

References 19 publications

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“…In layer (3), all nodes are shown with an N; they are fixed and are also identified with circles (Bardestani et al 2017). Another issue in this layer is that outputs are normalized.…”

Section: Fuzzy Inference Systemmentioning

confidence: 99%

“…It has only one fixed node which is shown with a P representing the sum. The output value of the layer is obtained by the sum of input signals (Basser et al 2014;Bardestani et al 2017).…”

Section: Fuzzy Inference Systemmentioning

confidence: 99%

“…It has also some advantages compared to other susceptibility mapping methods such as expert-knowledge-based GIS-MCDA. GIS-MCDA is sometimes criticized for the expert knowledge to be a major source of uncertainty among the results (Ş alap-Ayça and Jankowski 2016; Feizizadeh and Kienberger 2017;Erlacher et al 2017;Feizizadeh and Ghorbanzadeh 2017;Cabrera-Barona and Ghorbanzadeh 2018) or ordinary neural networks because they use if-then rules (Bardestani et al 2017). The proposed method does not apply expert opinions at any stage.…”

Section: Introductionmentioning

confidence: 99%

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A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

et al. 2018

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In this paper, we evaluate the predictive performance of an adaptive neuro-fuzzy inference system (ANFIS) using six different membership functions (MF). In combination with a geographic information system (GIS), ANFIS was used for land subsidence susceptibility mapping (LSSM) in the Marand plain, northwest Iran. This area is prone to droughts and low groundwater levels and subsequent land subsidence damages. Therefore, a land subsidence inventory database was created from an extensive field survey. Areas of land subsidence or areas showing initial signs of subsidence were used for training, while onethird of inventory database were reserved for testing and validation. The inventory database randomly divided into three different folds of the same size. One of the folds was chosen for testing and validation. Other two folds was used for training. This process repeated for every fold in the inventory dataset. Thereafter, land subsidence related factors, such as hydrological and topographical factors, were prepared as GIS layers. Areas susceptible to land subsidence were then analyzed using the ANFIS approach, and land subsidence susceptibility maps were created, whereby six different MFs were applied. Lastly, the results derived from each MF were validated with those areas of the land subsidence database that were not used for training. Receiver operating characteristics (ROC) curves were drawn for all LSSMs, and the areas under the curves were calculated. The ROC analyses for the six LSSMs yielded very high prediction values for two out of the six methods, namely the difference of DsigMF (0.958) and GaussMF (0.951). The integration of ANFIS and GIS generally led to high LSSM prediction accuracies. This study demonstrated that the choice of training dataset and the MF significantly affects the results.

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“…In layer (3), all nodes are shown with an N; they are fixed and are also identified with circles (Bardestani et al 2017). Another issue in this layer is that outputs are normalized.…”

Section: Fuzzy Inference Systemmentioning

confidence: 99%

“…It has only one fixed node which is shown with a P representing the sum. The output value of the layer is obtained by the sum of input signals (Basser et al 2014;Bardestani et al 2017).…”

Section: Fuzzy Inference Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

et al. 2018

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“…Besides these methods, artificial intelligence and machine learning as a quick and powerful tool [23][24][25] can be used to predict and manage the flood. Bardestani et al used ANFIS which is a combination of Neural Network and Fuzzy Logic in Water Resources [26].…”

Section: Rehabilitation System After Floodmentioning

confidence: 99%

Flood Management, Flood Forecasting and Warning System

Hajibabaei¹,

Ghasemi²

2017

IJSEA

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Flood phenomenon annually causes many human and financial losses so that flood has even had many losses in countries which are properly developed in river bank protection and construction of reservoir dams aiming to control flood. Today, controlling flood through its management is discussed and one of efficient and effective tools in this field is non-structural flood management as the complementary of structural ones that can have a very important role in controlling flood and consequently reducing the losses derived by that. Flood forecasting and warning systems are considered as one of the non-structural management tools which has given high importance within recent decades and providing information about flood and fast and easy reaction after receiving mentioned information to have minimum losses in flood areas as well as providing some information to manage water resources are of the aims of this system.

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“…Chamani and Rajaratnam also Chanson Performed experiments on the Skimming Flow regime and devolved experimental formulas to determine the energy loss rate in this regime [5] [6]. Some efforts has been made to use numerical simulation and machine learning in engineering [7] [8].Chen et al used finite volume method and utilized the k-ε turbulent model to determine flow turbulence in stepped spillways [9]. Tabbara et al simulated the flow over stepped spillway by the finite element method using ADINA software with the k-ε standard turbulence model [10].…”

Section: Introductionmentioning

confidence: 99%

Energy Loss Estimation and Flow Simulation in the skimming flow Regime of Stepped Spillways with Inclined Steps and End Sill: A Numerical Model

Hamedi¹,

Ketabdar²

2016

IJSEA

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Abstract:Energy dissipaters in dams consider as expensive structures; therefore, economic design of these structure are desirable.Stepped spillways can be use as energy dissipater to reduce the size and cost of the stilling basin. Hard experimental works, high-cost and time-consuming laboratory methods brought about some difficulties to determine the most-efficient design of the steps. This research deals with comparing the two-dimensional numerical simulation and experimental description in stepped spillways equipped with inclined steps and end sill together and presents a quick, trustworthy, economical, and numerical approach to designing the steps. In complicated geometries, simulation is more problematic than simulation of flow in horizontal steps, because it needs more precision around the end sills. Finite volume method and the k-ε standard model are proposed to simulate the flow pattern and evaluate the energy loss over stepped spillway. The flow pattern and velocity vectors resulted from numerical simulation is in a good agreement with the experimental results. Also, energy losses resulted from the numerical approach have been compared with laboratory measurements and demonstrate reasonable agreement.

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Predicting turbulent flow friction coefficient using ANFIS technique

Cited by 21 publications

References 19 publications

A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

Flood Management, Flood Forecasting and Warning System

Energy Loss Estimation and Flow Simulation in the skimming flow Regime of Stepped Spillways with Inclined Steps and End Sill: A Numerical Model

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