Predicting the Frequency of Marine Accidents by Navigators’ Watch Duty Time in South Korea Using LSTM

Choi, Jungyeon

doi:10.3390/app122211724

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…LSTM is proficient in capturing long-term dependencies and non-linear relationships from historical data, thereby enabling accurate prediction of future changes [45]. The LSTM also has a wide range of applications: it has been successfully applied to prediction in various fields such as medicine [30,[46][47][48], finance [49,50], transportation [51,52], acoustics [53], hydrology [54], and marine science [55]. From the above research, it is evident that ARIMA and LSTM share similar application areas.…”

Section: Regulated or Suggested Concentration Of Co 2 (Ppm)mentioning

confidence: 99%

Prediction and Comparison of In-Vehicle CO2 Concentration Based on ARIMA and LSTM Models

Han,

Lin,

Qin

2023

Applied Sciences

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An increase in the carbon dioxide (CO2) concentration within a vehicle can lead to a decrease in air quality, resulting in numerous adverse effects on the human body. Therefore, it is very important to know the in-vehicle CO2 concentration level and to accurately predict a concentration change. The purpose of this research is to investigate in-vehicle concentration levels of CO2, comparing the accuracy of an autoregressive integrated moving average (ARIMA) model and a long short-term memory (LSTM) model in predicting the change in CO2 concentration. We conducted a field test to obtain in-vehicle original concentration data of CO2 while driving, establishing a prediction model of CO2 concentration with ARIMA and LSTM. We selected mean absolute percentage error (MAPE) and root mean squared error (RMSE) as the evaluation indicators. The findings indicate the following: (1) With the vehicle windows closed and recirculation ventilation mode activated, in-vehicle CO2 concentration increases rapidly. During testing, CO2 accumulation rates were measured at 1.43 ppm/s for one occupant and 3.52 ppm/s for three occupants within a 20 min driving period. Average concentrations exceeded 1000 ppm, so it is recommended to improve ventilation promptly while driving. (2) The MAPE of ARIMA and LSTM prediction results are 0.46% and 0.56%, respectively. The RMSE results are 19.62 ppm and 22.76 ppm, respectively. The prediction results demonstrate that both models effectively forecast changes in a vehicle’s interior environment CO2, but the prediction accuracy of ARIMA is better than that of LSTM. The research findings provide theoretical guidance to traffic safety managers in selecting suitable models for predicting in-vehicle CO2 concentrations and establish an effective in-vehicle ventilation warning control system.

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Section: Regulated or Suggested Concentration Of Co 2 (Ppm)mentioning

confidence: 99%

Prediction and Comparison of In-Vehicle CO2 Concentration Based on ARIMA and LSTM Models

Han,

Lin,

Qin

2023

Applied Sciences

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Rice Price Forecasting Using GridSearchCVand LSTM

Setivani,

Handayani,

Geraldine

2023

2023 International Conference on Modeling &Amp;amp; E-Information Research, Artificial Learning and Digital Applications (ICMER

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Advancements in Deep Learning Techniques for Time Series Forecasting in Maritime Applications: A Comprehensive Review

Wang,

Guo,

She

et al. 2024

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The maritime industry is integral to global trade and heavily depends on precise forecasting to maintain efficiency, safety, and economic sustainability. Adopting deep learning for predictive analysis has markedly improved operational accuracy, cost efficiency, and decision-making. This technology facilitates advanced time series analysis, vital for optimizing maritime operations. This paper reviews deep learning applications in time series analysis within the maritime industry, focusing on three areas: ship operation-related, port operation-related, and shipping market-related topics. It provides a detailed overview of the existing literature on applications such as ship trajectory prediction, ship fuel consumption prediction, port throughput prediction, and shipping market prediction. The paper comprehensively examines the primary deep learning architectures used for time series forecasting in the maritime industry, categorizing them into four principal types. It systematically analyzes the advantages of deep learning architectures across different application scenarios and explores methodologies for selecting models based on specific requirements. Additionally, it analyzes data sources from the existing literature and suggests future research directions.

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Predicting the Frequency of Marine Accidents by Navigators’ Watch Duty Time in South Korea Using LSTM

Cited by 3 publications

References 21 publications

Prediction and Comparison of In-Vehicle CO2 Concentration Based on ARIMA and LSTM Models

Prediction and Comparison of In-Vehicle CO2 Concentration Based on ARIMA and LSTM Models

Rice Price Forecasting Using GridSearchCVand LSTM

Advancements in Deep Learning Techniques for Time Series Forecasting in Maritime Applications: A Comprehensive Review

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