Application of neuro-fuzzy approach in prediction of runup in swash zone

Bakhtyar, Roham; Bakhtiary, Abbas Yeganeh; Ghaheri, Abbas

doi:10.1016/j.apor.2008.02.004

Cited by 31 publications

(15 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous machine learning work has focused on predicting run-up and swash, but only for engineered structures, impermeable slopes and/or for laboratory experiments (e.g. Kazeminezhad and Etemad-Shahidi, 2015;Bonakdar and Etemad-Shahidi, 2011;Bakhtyar et al, 2008;Abolfathi et al, 2016) and not on natural beaches apart from Vousdoukas et al (2011), which used artificial neural networks (ANNs) for shoreline contour elevation (which includes the wave run-up), on a natural beach in Portugal. In this study we focus on the use an evolutionary technique, genetic programming (GP), to solve the symbolic regression problem of developing new, optimized swash predictors.…”

Section: Introductionmentioning

confidence: 99%

The use of genetic programming to develop a predictor of swash excursion on sandy beaches

Passarella

Goldstein

Muro

et al. 2018

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. We use genetic programming (GP), a type of machine learning (ML) approach, to predict the total and infragravity swash excursion using previously published data sets that have been used extensively in swash prediction studies. Three previously published works with a range of new conditions are added to this data set to extend the range of measured swash conditions. Using this newly compiled data set we demonstrate that a ML approach can reduce the prediction errors compared to well-established parameterizations and therefore it may improve coastal hazards assessment (e.g. coastal inundation). Predictors obtained using GP can also be physically sound and replicate the functionality and dependencies of previous published formulas. Overall, we show that ML techniques are capable of both improving predictability (compared to classical regression approaches) and providing physical insight into coastal processes.

show abstract

Section: Introductionmentioning

confidence: 99%

The use of genetic programming to develop a predictor of swash excursion on sandy beaches

Passarella

Goldstein

Muro

et al. 2018

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…In order to evaluate the results of neural network model, they have been compared with the results in [6] in terms of normalized Mean Squared Error (MSE), which is de ned as follows:…”

Section: Methodsmentioning

confidence: 99%

“…The new techniques of wave run-up investigation were reported by Bakhtyar et al They used neural networks to present an appropriate prediction method for wave run-up on each armor unit based on its considerable roughness and permeability [6].…”

Section: Introductionmentioning

confidence: 99%

Application of Neural Network Models to Improve Prediction Accuracy of Wave Run-up on Antifer Covered Breakwater

2017

View full text Add to dashboard Cite

KEYWORDSWave run-up; Breakwater; Arti cial neural network; Multi-Layer Perceptron (MLP); MSE.Abstract. The primary goal of this study is to present a better way in terms of cost and experimenting duration, instead of using experimental ways for investigating the wave run-up (Ru) over rubble-mound breakwater and examining the e ect of placement pattern of antifer units on the amount of wave run-up. To do so, Arti cial Neural Networks (ANNs) are suggested. For the sake of comparison, the proposed modeling is put into contrast by the ones obtained via other approaches in the literature. The Multi-Layer Perceptron (MLP) is selected as the arti cial neural network is exerted in this study. In the designed neural network, the numbers of inputs and outputs are selected as four and one, respectively. On the other hand, the number of neurons in the single hidden layer of the network should be determined by trial and error considering the Mean Square Error (MSE) of the training and validation samples, which has been chosen as seven in this paper. The regression equations and MSE for the results obtained by ANN are presented in this paper and are compared with other models in the literature. Moreover, the regular placement is preferred to other placement patterns due to its less MSE obtained by ANN.

show abstract

“…It was found that the prediction performances of ANFIS model is better than traditional multiple linear regression model. ANFIS has also been used in the field of science and technology by many researchers (Guler & Ubeyli, 2004;Zaheeruddin & Garima, 2006;Naadimutha et al, 2007;Cakmakci, 2007;Bakhtyar et al, 2008;Wang & Elhag, 2008;Khajeh et al, 2009;Radulovic & Rankovic, 2010;Yan et al, 2010;Ata & Kocyigit, 2010;Sargolzaei & Kianifar, 2010;Yilmaz & Kaynar, 2011;Mohammadi et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Backbreak in Open Pit Blasting by Adaptive Neuro-Fuzzy Inference System / Prognozowanie Spękań Skał Przy Pracach Strzałowych W Kopalniach Odkrywkowych Przy Użyciu Metod Neuronowych I Wnioskowania Rozmytego (Anfis) Zastosowanych W Modelu Adaptywnym

Bazzazi¹,

Esmaeili²

2012

Archives of Mining Sciences

View full text Add to dashboard Cite

Adaptive neuro-fuzzy inference system (ANFIS) is powerful model in solving complex problems. Since ANFIS has the potential of solving nonlinear problem and can easily achieve the input-output mapping, it is perfect to be used for solving the predicting problem. Backbreak is one of the undesirable effects of blasting operations causing instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling. In this paper, ANFIS was applied to predict backbreak in Sangan iron mine of Iran. The performance of the model was assessed through the root mean squared error (RMSE), the variance account for (VAF) and the correlation coefficient (R 2 ) computed from the measured of backbreak and model-predicted values of the dependent variables. The RMSE, VAF, R 2 indices were calculated 0.6, 0.94 and 0.95 for ANFIS model. As results, these indices revealed that the ANFIS model has very good prediction performance.Keywords: blasting, backbreak, adaptive neuro-fuzzy inference system, Sangan iron mine Adaptywny system wnioskowania wykorzystujący elementy sieci neuronowych i logiki rozmytej (AN-FIS) stanowi potężny narzędzie do rozwiązywania złożonych problemów. Ponieważ model ANFIS może być wykorzystywany do rozwiązywania problemów nieliniowych i umożliwia wygodne przedstawienie problemu w formie: wejście -wyjście, jest idealnym narzędziem do rozwiązywania problemów związanych z prognozowaniem. Pękanie skał w odkrywce jest jednym z niekorzystnych skutków prowadzenia prac strzałowych, powoduje niestabilność ścian, uszkodzenia maszyn i urządzeń, nieodpowiednią fragmentację skał oraz prowadzi do obniżenia efektywności wierceń. W pracy przedstawiono zastosowanie systemu ANFIS do prognozowania pękań skał w kopalni rud żelaza w Sangan (Iran). Działanie modelu zbadano na podstawie wartości błędu średniokwadratowego (RMSE), wariancji (VAF) i współczynnika korelacji Słowa kluczowe: prace strzałowe, pękanie skał, system wnioskowania wykorzystujący elementy sieci neuronowych i logiki rozmytej, kopalnia rud żelaza Sangan IntroductionAlthough the main purpose of blasting in open pit mines is rock breakage and finally facilitating in loading operations, the other effects of blasting such as ground vibration, fly rock and backbreak should be considered. Backbreak can be defined as breakage behind the last row of holes (Konya & Walter, 1991). This phenomenon may cause instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling (Bazzazi, 2005). Several factors leading to backbreak have been described by various researchers. Konya and Walter (1991) described some of the causes for backbreak such as excessive burden and stiff benches, long stemming depth on stiff benches and improper timing delay. Gate et al. (2005) believed that a combination of factors in the blasting such as overstemming of the shot holes and short timing delays in the firing sequence may have led to the severe backbreak. Moreover, the adverse geological structure app...

show abstract

Application of neuro-fuzzy approach in prediction of runup in swash zone

Cited by 31 publications

References 23 publications

The use of genetic programming to develop a predictor of swash excursion on sandy beaches

The use of genetic programming to develop a predictor of swash excursion on sandy beaches

Application of Neural Network Models to Improve Prediction Accuracy of Wave Run-up on Antifer Covered Breakwater

Prediction of Backbreak in Open Pit Blasting by Adaptive Neuro-Fuzzy Inference System / Prognozowanie Spękań Skał Przy Pracach Strzałowych W Kopalniach Odkrywkowych Przy Użyciu Metod Neuronowych I Wnioskowania Rozmytego (Anfis) Zastosowanych W Modelu Adaptywnym

Contact Info

Product

Resources

About