Wave height forecasting in Dayyer, the Persian Gulf

Kamranzad, Bahareh; Etemad‐Shahidi, Amir; Kazeminezhad, Mohammad Hossein

doi:10.1016/j.oceaneng.2010.10.004

Cited by 83 publications

(29 citation statements)

References 23 publications

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“…Our preliminary modelling showed that even though it is possible to have ANN models with three outputs, the accuracy generally decreases due to the complexity of the network. This finding is in line with the existing knowledge about the performance of ANN networks (Asma et al, 2012;Kamranzad et al, 2011;Zamani et al, 2008).…”

Section: Study Area and Evaluation Of The Datasupporting

confidence: 91%

Wave energy forecasting using artificial neural networks in the Caspian Sea

Hadadpour

Etemad‐Shahidi

Kamranzad³

2014

Proceedings of the Institution of Civil Engineers - Maritime En

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Providing energy without unfavourable impact on the environment is an important issue that is considered by societies. This paper focuses on forecasting the wave energy over horizons of 1-12 h, in the southern part of the Caspian Sea. For this purpose, an artificial neural network was used to obtain the wave energy flux using two different methods. First, the components of wave energy flux, including the significant wave height and peak wave period were predicted separately and the wave energy flux was calculated by combining them; and second, the wave c peak enhancement factor r density of seawater i forecasting time horizon (from 1 to 12 h) t time Proceedings of the Institution of Civil Engineers

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Section: Study Area and Evaluation Of The Datasupporting

confidence: 91%

Wave energy forecasting using artificial neural networks in the Caspian Sea

Hadadpour

Etemad‐Shahidi

Kamranzad³

2014

Proceedings of the Institution of Civil Engineers - Maritime En

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“…ANN has been the popular method for researchers to help them to solve prediction problem [21]. Architecture ANN has layer and neuron.…”

Section: Wide Genetic Algorithm Artificial Neural Networkmentioning

confidence: 99%

Automatic Identification System (AIS) based Ship Heading Prediction using Artificial Neural Network and Wide Genetic Algorithm

2019

Maritime Safety International Conference (MASTIC 2018)

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International Maritime Organization (IMO) requires ship with more than 300 gross tonnages to have Automatic Identification System (AIS) onboard. AIS is used as a navigational aid to ensure the safety of ship operation including collision avoidance, vessel tracking, and accident investigation. AIS Transponder is installed onboard ship and transmitted data will be received by the base station as AIS data receiver. AIS Transponder provides information for instance a unique identification, position, course, and speed. Then those data will be utilized for many applications. Ship's movement information obtained from AIS such as position and heading can be utilized as an early warning to avoid the event of the collision. In some cases, ship heading data is unidentified caused by transponder incompatibility and mistakenly conducting setup of AIS transponder. To solve the problem, extrapolation method can be used to predict the ship's heading. However, this method has the disadvantage since it only considers the location of ships while the distinctive area near the port is ignored. This paper aims to develop a method to predict the ship heading based on AIS historical data of ship's movement using Artificial Intelligence (AI). Artificial Neural Network (ANN) has been chosen as AI prediction model. There are two pre-processing should do before training process such a down sampling and sliding window. Wide Genetic Algorithm (WGA) is used for ANN training process to create ANN model. WGA-ANN computes the ship heading by predicting the next geolocation of ship.

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“…The ANN is a nonlinear mathematical model with the ability to simulate the biological neurons of the human brain (Kamranzad, Etemad-Shahidi, & Kazeminezhad, 2011). Each neuron input in hidden layers or output layer is multiplied by a corresponding interconnection weight, and then a bias (threshold) is added to the sum of the products:…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Estimation of scour depth below free overfall spillways using multivariate adaptive regression splines and artificial neural networks

Samadi

Jabbari

Azamathulla

et al. 2015

Engineering Applications of Computational Fluid Mechanics

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Erosion and scouring caused by the outflow jets of hydraulic structures is one of the most important topics in hydraulic engineering. In free overfall spillways, a water jet impacts the erodible downstream bed almost vertically and creates a scour hole. The scour hole can affect the safety and stability of the dam. In the present paper, the multivariate adaptive regression splines (MARS) approach has been adopted as a new soft computing tool for estimating the equilibrium scour depth below free overfall spillways. Using experimental data and dimensionless parameters, the MARS model has been developed to predict scour depth. Results obtained from the MARS approach were compared with those from the artificial neural network (ANN) and decision-tree algorithm. Statistical indicators demonstrated that the MARS approach had a good performance and presented competitive results, slightly better than ANN, for the prediction of this phenomenon.

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Wave height forecasting in Dayyer, the Persian Gulf

Cited by 83 publications

References 23 publications

Wave energy forecasting using artificial neural networks in the Caspian Sea

Wave energy forecasting using artificial neural networks in the Caspian Sea

Automatic Identification System (AIS) based Ship Heading Prediction using Artificial Neural Network and Wide Genetic Algorithm

Estimation of scour depth below free overfall spillways using multivariate adaptive regression splines and artificial neural networks

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