Prediction of significant wave height in The Java Sea using Artificial Neural Network

Rizianiza, Illa; Aisjah, Aulia Siti

doi:10.1109/isitia.2015.7219944

Cited by 9 publications

(2 citation statements)

References 13 publications

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“…Method Dataset Inputs Results ANNs, SVRs, M5 decision tree algorithm, and Recurrent Neural Networks (RNNs) including Long-Short-Term Memory (LSTM) models are used to predict wave heights, as per studies by Duong et al (2023) and Rizianiza and Aisjah (2015). These ML techniques have shown reliable wave prediction capabilities, maintaining accuracy up to 72 h ahead (Jain and Deo, 2008).…”

Section: Referencesmentioning

confidence: 99%

Machine learning application in modelling marine and coastal phenomena: a critical review

Pourzangbar,

Jalali,

Brocchini

2023

Front. Environ. Eng.

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This study provides an extensive review of over 200 journal papers focusing on Machine Learning (ML) algorithms’ use for promoting a sustainable management of the marine and coastal environments. The research covers various facets of ML algorithms, including data preprocessing and handling, modeling algorithms for distinct phenomena, model evaluation, and use of dynamic and integrated models. Given that machine learning modeling relies on experience or trial-and-error, examining previous applications in marine and coastal modeling is proven to be beneficial. The performance of different ML methods used to predict wave heights was analyzed to ascertain which method was superior with various datasets. The analysis of these papers revealed that properly developed ML methods could successfully be applied to multiple aspects. Areas of application include data collection and analysis, pollutant and sediment transport, image processing and deep learning, and identification of potential regions for aquaculture and wave energy activities. Additionally, ML methods aid in structural design and optimization and in the prediction and classification of oceanographic parameters. However, despite their potential advantages, dynamic and integrated ML models remain underutilized in marine projects. This research provides insights into ML’s application and invites future investigations to exploit ML’s untapped potential in marine and coastal sustainability.

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Section: Referencesmentioning

confidence: 99%

Machine learning application in modelling marine and coastal phenomena: a critical review

Pourzangbar,

Jalali,

Brocchini

2023

Front. Environ. Eng.

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“…ML method also has been applied to predict application in marine technology such as tidal wave [11], wave height [12] and ship's heading [13]. However, ANN has used Backpropagation (BP) for training process [14]. BP has a weakness in training process since sometimes ANN will generate a random weight value that closes to local optimum [15].…”

Section: Introductionmentioning

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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Significant Wave Height Prediction based on Wavelet Graph Neural Network

Chen

Liu

Zhang

et al. 2021

2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI)

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Prediction of significant wave height in The Java Sea using Artificial Neural Network

Cited by 9 publications

References 13 publications

Machine learning application in modelling marine and coastal phenomena: a critical review

Machine learning application in modelling marine and coastal phenomena: a critical review

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

Significant Wave Height Prediction based on Wavelet Graph Neural Network

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