Izvorni znanstveni članak
https://doi.org/10.21278/brod77311
Study on the prediction performance of ship motion in waves by LSTM under missing data
Jiaye Gong
; College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, China
Pengsheng Ni
; College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, China
Zheng Fu
; College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, China
*
Yongzhi Qin
; College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, China
* Dopisni autor.
Sažetak
Ship motion prediction is essential in marine engineering, but missing data caused by sensor faults or signal interruptions often degrades the accuracy of long short-term memory (LSTM) models. This study investigates how different missing data rates and imputation methods affect LSTM prediction performance. A ship-motion dataset under various speeds and wave conditions was used to examine model feasibility and hyperparameter sensitivity. Traditional filling strategies, including zero and mean filling, were compared under missing data scenarios. Results show that data loss significantly reduces prediction accuracy. The mean-filling method generally performs better than zero-filling, though its effectiveness decreases with higher data diversity. Proper data clustering can effectively enhance its performance.
Ključne riječi
Ship motion prediction; LSTM; missing data; data imputation
Hrčak ID:
345651
URI
Datum izdavanja:
1.7.2026.
Posjeta: 199 *