Comparison of ANFIS and RBF models in daily stream flow forecasting

Khadangi, Ehsan; Madvar, Hossein Riahi; Ebadzadeh, Mohammad Mehdi

doi:10.1109/ic4.2009.4909240

Cited by 15 publications

(7 citation statements)

References 7 publications

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“…The result shows that the ANFIS model produced better performance than the ANN model. Khadangi et al developed the ANFIS and Radial Base Function (RBF) model for daily river forecasting [58]. The performance of both models was compared, and the result indicates that the ANFIS model had better performance compared to the RBF model in river forecasting.…”

Section: Anfismentioning

confidence: 99%

Methods for Hydropower Discharge Prediction: A Review

Anuar

Khan

Ramli

2021

Malaysian J. Sci. Adv. Tech.

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Hydropower has a well-established position in the energy sector, and it is very important to promote the further growth of this energy resource, especially in developing countries. As a result, the water flow modeling sector becomes a key area of study due to the importance of water resource management. Where, the precise forecasts of hydropower water flow are important for effective management practices. This paper provides a review on hydropower system. An overview on the method and techniques used to simulate water forecasting is made to provide further understanding. An analysis was also made to identify the techniques that provide high accuracy in hydropower flow forecasting.

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Section: Anfismentioning

confidence: 99%

Methods for Hydropower Discharge Prediction: A Review

Anuar

Khan

Ramli

2021

Malaysian J. Sci. Adv. Tech.

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“…In spite of the MLP model, in the RBF model there is only one hidden layer, which is composed of an arbitrary number of units. Any unit in a hidden layer has its radial basis function as an activation function (Khadangi, Madvar, & Ebadzadeh, 2009). The output of the hidden layer Z j (x) estimates the distance between input to the center of the radial basis function and images this distance on to the activation function as follows (Khadangi et al, 2009):…”

Section: Radial Basis Functionsmentioning

confidence: 99%

Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry

Riahi-Madvar

Dehghani

Seifi

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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The main aims and contributions of the present paper are to use new soft computing methods for the simulation of scour geometry (depth/height and locations) in a comparative framework. Five models were used for the prediction of the dimension and location of the scour pit. The five developed models in this study are multilayer perceptron (MLP) neural network, radial basis functions (RBF) neural network, adaptive neuro fuzzy inference systems (ANFIS), multiple linear regression (MLR), and multiple non-linear regression (MNLR) in comparison with empirical equations. Four non-dimensional geometry parameters of scour hole shape are predicted by these models including the maximum scour depth (S), the distance of S from the weir (XS), the maximum height of downstream deposited sediments (hd), and distance of hd from the weir (XD). The best results over train data derived for XS/Z and hd/Z by the MLP model with R 2 are 0.95 and 0.96 respectively; the best predictions for S/Z and XD/Z are from the ANFIS model with R 2 0.91 and 0.96 respectively. The results indicate that the application of MLP and ANFIS results in the accurate prediction of scour geometry for the designing of stable grade control structures in alluvial irrigation channels.

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“…ANFIS has been applied in several forecasting domains such as electricity price forecasting [11], weather forecasting [12], solar radiation data forecasting [13], daily stream flow forecasting [14], internet traffic time series forecasting [15] and demand forecasting [16]. In this paper, the membership function and fuzzy rule is obtained by historical solar power generation.…”

Section: Adaptive Network-based Fuzzy Inference Systemmentioning

confidence: 99%

“…At the fifth layer, the consequent parameters are calculated using a least-squares regression method [13]. During each epoch, the output of the ANFIS is calculated and after calculation of the error, the ratio of error is back propagated over every layers and those values are adapted based on error descent gradient method [14].…”

Section: Adaptive Network-based Fuzzy Inference Systemmentioning

confidence: 99%

Short-Term Solar Power Forecasting Using the Adaptive Network-Based Fuzzy Inference System

Chang¹,

Miao²

2015

Proceedings of the International Conference on Chemical, Material and Food Engineering

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Abstract-This paper proposes an adaptive network-based fuzzy inference system (ANFIS) based forecasting method for short-term solar power forecasting. An accurate forecasting method for power generation of the photovoltaic (PV) system is urgent needed under the relevant issues associated with the high penetration of solar power in the electricity system. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of solar power generation of a PV system installed on the St. John's University of Taiwan. Good agreements between the realistic values and forecasting values are obtained; the test results show the proposed forecasting method is accurate.

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Comparison of ANFIS and RBF models in daily stream flow forecasting

Cited by 15 publications

References 7 publications

Methods for Hydropower Discharge Prediction: A Review

Methods for Hydropower Discharge Prediction: A Review

Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry

Short-Term Solar Power Forecasting Using the Adaptive Network-Based Fuzzy Inference System

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