Improving the accuracy of ship emission inventories based on LSTM-CNN model

Liangqi Ren; Junru Liang

doi:10.1117/12.3012331

21 December 2023 Improving the accuracy of ship emission inventories based on LSTM-CNN model

Liangqi Ren, Junru Liang

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129700M (2023) https://doi.org/10.1117/12.3012331
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

Based on the AIS data, this study proposes a method to improve the accuracy of ship emission model prediction by using interpolation preprocessing and LSTM-CNN model prediction. The method uses the third spline interpolation preprocessing method and LSTM-CNN time-series prediction model to predict pollutant emissions. Firstly, the cubic spline interpolation method is used to interpolate the data at different time intervals. The optimal interpolation interval is selected. Next, use the data interpolated with the optimal interval distance as the dataset, the trajectory and navigation status data of the ship are predicted using the LSTM-CNN model. Finally, emissions were estimated based on the Ship Traffic Emission Assessment Model (STEAM). The study shows that the error between the interpolated emissions and the real emissions is 1.4587%, and the prediction using the interpolated data is 1.2702% from the original emissions, with an error reduction of 0.1885%.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Liangqi Ren and Junru Liang "Improving the accuracy of ship emission inventories based on LSTM-CNN model", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129700M (21 December 2023); https://doi.org/10.1117/12.3012331

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