Prediction of wave transmission over submerged reef of tandem breakwater using PSO-SVM and PSO-ANN techniques

Kuntoji, Geetha; Rao, M. V. Nageswara; Rao, Subba

doi:10.1080/09715010.2018.1482796

Cited by 23 publications

(8 citation statements)

References 26 publications

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“…They optimised the existing ANN (Zanuttigh et al (2014)) to predict small discharge values. Kuntoji et al (2020) used Particle Swarm Optimisation based ANN and Support Vector Machine to predict wave transmission on tandem breakwaters [13]. Using different statistical parameters performance of each model is calculated.…”

Section: Artificial Intelligencementioning

confidence: 99%

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Effect of data normalisation in estimating wave overtopping discharge parameter of semicircular breakwater using ANN and Random Forest.

Krishna

Mane

Rao

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Breakwaters are the structures constructed in the coastal areas to maintain calm inside the port or prevent beach erosion. Semi-circular Breakwater (SCB) is an innovative type of Breakwater made of hollow caisson on a base slab with or without perforations. In this study, the wave overtopping discharge parameter of an SCB is estimated using Artificial Neural Network and Random Forest. The data is collected and used in the current research from an experimental investigation conducted in the Wave Mechanics Laboratory of the Department of Water Resources and Ocean Engineering (WROE), NITK Surathkal. Using this experimental data, the ANN and Random Forest models are developed for the prediction of the wave overtopping discharge parameter of an SCB. The performance of the models is evaluated using different statistical parameters. Data with and without normalisation are used separately to check the effect of normalisation in the prediction of wave overtopping discharge parameter using ANN and Random Forest. From the results, it is found that ANN gives better results when the data is normalised. The performance of Random Forest is independent of the data normalisation.

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Section: Artificial Intelligencementioning

confidence: 99%

“…Case 2: Normalised data is used. In this case, the given data is normalised between 0 to 1 using the following equation [22].…”

Section: Development Of Modelmentioning

confidence: 99%

Effect of data normalisation in estimating wave overtopping discharge parameter of semicircular breakwater using ANN and Random Forest.

Krishna

Mane

Rao

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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“…1], n 1 and n 2 are the cognitive/social constant accelerations, while, c 1 and c 2 are the vectors lie between [0, 1]. Some recent applications of PSO are parameter estimation (O ¨zsoy et al 2020), robotics (Mai et al 2019;Yang et al 2019), nonlinear electric circuits (Qu et al 2020), systems of equation-based physical models (Kuntoji et al 2020), and optimization of permanent magnets synchronous motor (Mesloub et al 2020).…”

Section: Optimization: Pso-ipmmentioning

confidence: 99%

Neuro-swarm computational heuristic for solving a nonlinear second-order coupled Emden–Fowler model

Sabir

Raja²,

Băleanu

et al. 2022

Soft Comput

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The aim of the current study is to present the numerical solutions of a nonlinear second-order coupled Emden–Fowler equation by developing a neuro-swarming-based computing intelligent solver. The feedforward artificial neural networks (ANNs) are used for modelling, and optimization is carried out by the local/global search competences of particle swarm optimization (PSO) aided with capability of interior-point method (IPM), i.e., ANNs-PSO-IPM. In ANNs-PSO-IPM, a mean square error-based objective function is designed for nonlinear second-order coupled Emden–Fowler (EF) equations and then optimized using the combination of PSO-IPM. The inspiration to present the ANNs-PSO-IPM comes with a motive to depict a viable, detailed and consistent framework to tackle with such stiff/nonlinear second-order coupled EF system. The ANNs-PSO-IP scheme is verified for different examples of the second-order nonlinear-coupled EF equations. The achieved numerical outcomes for single as well as multiple trials of ANNs-PSO-IPM are incorporated to validate the reliability, viability and accuracy.

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“…In recent years, the combined effect of the evolutionary algorithm is developed using optimization technique, particle swarm optimization (PSO) [2] with ANN as an emerging tool in various fields. The (PSO-ANN) technique is used to identify the surface settlement due to tunneling [13], to check the floating type breakwater efficiency [14], to predict wave transmission of tandem breakwater [15] and fault prediction of objectoriented systems [16].…”

Section: Related Workmentioning

confidence: 99%

Application of Particle Swarm based Neural Network to Predict Scour Depth around the Bridge Pier

M¹,

Kuntoji²,

Manu³

et al. 2019

IJISA

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Scour around the bridge pier is one of the major factors which affect the safety and stability of the bridge structure. Due to the presence of complexity in the scour mechanism, there is no common and simple method to estimate the scour depth. The present paper gives an idea of hybridizing two techniques such as an artificial neural network with swarm intelligence technique particle swarm optimization to estimate the scour depth around the bridge pier and abbreviated as PSO-ANN. The present discussion covers the estimation of scour depth for clear water and live bed scour condition around circular and rectangular pier shapes. The independent variables, Sediment size (d 50), sediment quantity (Sq), velocity (u) and time (t) are used as input to develop the models to estimate or quantify a dependent variable scour depth (ds). The efficiency and accuracy of the model are measured using model performances indicators such as Correlation Coefficient (CC), Normalized Root Mean Square Error (NRMSE), Nash Sutcliffe Error (NSE), and Normalized Mean Bias (NMB). The predicted results of both the models are compared with each other and also compared with measured scour depth. The study concludes that the proposed PSO-ANN model is suitable to estimate the scour depth in both the cases for circular and rectangular pier shapes.

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Prediction of wave transmission over submerged reef of tandem breakwater using PSO-SVM and PSO-ANN techniques

Cited by 23 publications

References 26 publications

Effect of data normalisation in estimating wave overtopping discharge parameter of semicircular breakwater using ANN and Random Forest.

Effect of data normalisation in estimating wave overtopping discharge parameter of semicircular breakwater using ANN and Random Forest.

Neuro-swarm computational heuristic for solving a nonlinear second-order coupled Emden–Fowler model

Application of Particle Swarm based Neural Network to Predict Scour Depth around the Bridge Pier

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