An ANFIS-based Approach for Predicting the Manning Roughness Coefficient in Alluvial Channels at the Bank-full Stage

Bahramifar, A; Shirkhani, R; Mohammadi, Mirali

doi:10.5829/idosi.ije.2013.26.02b.08

Cited by 16 publications

(9 citation statements)

References 36 publications

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“…Over the last decades, artificial intelligent techniques have been introduced and widely applied in hydrological studies as powerful alternative modelling tools, such as Artificial Neural Network (ANN) [2]-[6], and fuzzy inference system (FIS) [7]- [9]. In addition, Shamseldin [10] (1997), Kumar et al [11] and Mutlu et al [12] compared ANNs with different input variables for runoff simulation.…”

Section: Introductionmentioning

confidence: 99%

“…Computer simulation models of watershed hydrology and artificial intelligent techniques are widely used for runoff simulation and forecasting. The use of watershed models is increasing due to the growing demands of improving runoff quantity.Over the last decades, artificial intelligent techniques have been introduced and widely applied in hydrological studies as powerful alternative modelling tools, such as Artificial Neural Network (ANN) [2]-[6], and fuzzy inference system (FIS) [7]- [9]. In addition, Shamseldin [10] (1997), Kumar et al [11] and Mutlu et al [12] compared ANNs with different input variables for runoff simulation.…”

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confidence: 99%

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A Comparison of ANN and HSPF Models for Runoff Simulation in Balkhichai River Watershed, Iran

Amirhossien

Alireza

Javan

et al. 2015

AJCC

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In this study, the capability of two different types of models including Hydrological Simulation Program-Fortran (HSPF) as a process-based model and ANN as a data-driven model in simulating runoff was evaluated. The considered area is the Balkhichai River watershed in northwest of Iran. HSPF is a semi-distributed deterministic, continuous and physically-based model that can simulate the hydrologic cycle, associated water quality and quantity and process on pervious and impervious land surfaces and streams. Artificial neural network (ANN) is probably the most successful learning machine technique with flexible mathematical structure which is capable of identifying complex non-linear relationships between input and output data without attempting to reach the understanding of the nature of the phenomena. Statistical approach depending on cross-, auto-and partial-autocorrelation of the observed data is used as a good alternative to the trial and error method in identifying model inputs. The performances of ANN and HSPF models in calibration and validation stages are compared with the observed runoff values in order to identify the best fit forecasting model based upon a number of selected performance criteria. Results of runoff simulation indicated that the simulated runoff by ANN was generally closer to the observed values than those predicted by HSPF. KeywordsHSPF Model, Artificial Neural Network (ANN), Runoff Simulation, Balkhichai River Watershed * Corresponding author. F. Amirhossien et al. 204 IntroductionStreamflow is one of the most important processes in the hydrological cycle and its prediction is vital for water resources management and planning [1]. Computer simulation models of watershed hydrology and artificial intelligent techniques are widely used for runoff simulation and forecasting. The use of watershed models is increasing due to the growing demands of improving runoff quantity.Over the last decades, artificial intelligent techniques have been introduced and widely applied in hydrological studies as powerful alternative modelling tools, such as Artificial Neural Network (ANN) [2]-[6], and fuzzy inference system (FIS) [7]- [9]. In addition, Shamseldin [10] (1997), Kumar et al. [11] and Mutlu et al. [12] compared ANNs with different input variables for runoff simulation. The comparisons showed that the ANN models applying both with rainfall and discharge as input variables gave better results than the models with rainfall as the input. When the model utilizes of rainfall values as the input variables, the simulated hydrographs do not match the measured hydrographs so well [13] [14]. Although better fits between the simulated and measured hydrographs have been reported in other studies, where additional variables such as temperature [15], evaporation [16] and, soil moisture [17] have been included as inputs for the ANN model.HSPF is a semi-distributed, conceptual model that combines spatially distributed physical attributes into the hydrologic response units. In this model, surface runoff is simula...

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

A Comparison of ANN and HSPF Models for Runoff Simulation in Balkhichai River Watershed, Iran

Amirhossien

Alireza

Javan

et al. 2015

AJCC

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“…When the process consequent parameters improve can be reduce the overall quadratic operational cost. The analyzing and mathematical observe condition of the hybrid-learning algorithm will show it in [14], [15].…”

Section: Figure 3 Triangular Membership Functionmentioning

confidence: 94%

“…The membership function can be any appropriate function such as; Gaussian, trapezoidal, generalized bell and triangular [14]. The membership function in this paper shows in Figure 3.…”

Section: Figure 3 Triangular Membership Functionmentioning

confidence: 99%

Application of ANFIS for Distance Relay Protection in Transmission Line

Azriyenni

Mustafa

2015

IJECE

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The techniques hybrid intelligent was introduced in transmission protection that usage in electric power systems. There was applied ANFIS for distance relay protection particularly for transmission line. If a fault occurs during the transmission line identification caused by unwanted fault thus the power delivery to the consumer becomes not going well. Therefore, it would need to provide an alternative solution to fix this problem. The objective of this paper uses impedance transmission line to determine how long the channel spacing will be protected by distance relay. It has been distance relays when fault occurs in transmission line with the application Sugeno ANFIS. The simulation shows it excellent testing results can be contributed to an alternate algorithm that it has good performance to protecting system in transmission line. This application used by using software Matlab.

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“…Various dynamic models with their comprehensive study is optimized [9,10]. MR Damper is the potential vibration control system for seismic mitigation has more advantages than other semi-active control devices [11]. The unique advantage is when increase in input current, the *Corresponding Author Email: danielckarunya@gmail.com (C. Daniel) damping force also increased.…”

Section: Introductionmentioning

confidence: 99%

Seismic Mitigation of Building Frames using Magnetorheological Damper (TECHNICAL NOTE)

Cruze

Hemalatha

Sarala

et al. 2019

IJE

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A B S T R A C TThe present study focusses on the damping force control of shear mode magnetorheological (MR) damper for seismic mitigations. Therefore, the semi-active MR damper which can control the vibration is analyzed both experimentally and numerically. Carbonyl iron is used as the magnetic particle and Castrol Magnetec oil as carrier fluid throughout the study. MR damper is designed and fabricated, and its damping force was evaluated experimentally at 2.5 A-10 V. Shear mode MR Damper is tested in universal testing machine using time history loading. The model was numerically analyzed using Newmark's method for nonlinear system in MATLAB to control the three storey model building frame taken from the literature. The result indicates that 49.42% reduction in displacement at the second storey and 48.14% in the third storey, respectively. Maximum reduction was observed when damper was kept in the ground floor. The maximum force observed for the MR Damper is 0.777 kN.

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An ANFIS-based Approach for Predicting the Manning Roughness Coefficient in Alluvial Channels at the Bank-full Stage

Cited by 16 publications

References 36 publications

A Comparison of ANN and HSPF Models for Runoff Simulation in Balkhichai River Watershed, Iran

A Comparison of ANN and HSPF Models for Runoff Simulation in Balkhichai River Watershed, Iran

Application of ANFIS for Distance Relay Protection in Transmission Line

Seismic Mitigation of Building Frames using Magnetorheological Damper (TECHNICAL NOTE)

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