Wave Height Prediction Suitable for Maritime Transportation Based on Green Ocean of Things
2023 (English)In: IEEE Transactions on Artificial Intelligence, ISSN 2691-4581, Vol. 4, no 2, p. 328-337Article in journal (Refereed) Published
Abstract [en]
Nowadays, the application fields of the Internet of Things (IoT) involve all aspects. This article combines ocean research with the IoT, in order to investigate the wave height prediction to assist ships to improve the economy and safety of maritime transportation and proposes an ocean IoT Green Ocean of Things (GOoT) with a green and low-carbon concept. In the wave height prediction, we apply a hybrid model (EMD-TCN) combining the temporal convolutional network (TCN) and the empirical mode decomposition (EMD) to the buoy observation data. We then compare it with TCN, long short-term memory (LSTM), and hybrid model EMD-LSTM. By testing the data of eight selected NDBC buoys distributed in different sea areas, the effectiveness of the EMD-TCN hybrid model in wave height prediction is verified. The hysteresis problem in previous wave height prediction research is eliminated, while improving the accuracy of the wave height prediction. In the 24 h, 36 h, and 48 h wave height prediction, the minimum mean absolute errors are 0.1265, 0.1689, and 0.1963, respectively; the maximum coefficient of determination are 0.9388, 0.9019, and 0.8712, respectively. In addition, in the short-term prediction, the EMD-TCN hybrid model also performs well, and has strong versatility. © 2020 IEEE.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2023. Vol. 4, no 2, p. 328-337
Keywords [en]
Empirical mode decomposition (EMD), Internet of Things (IoT), maritime transportation, temporal convolutional network (TCN), wave height prediction, Convolution, Forecasting, Internet of things, Long short-term memory, Oceanography, Time series analysis, Water waves, Convolutional networks, Empirical Mode Decomposition, Internet of thing, Marine vehicles, Ocean, Predictive models, Temporal convolutional network, Time-series analysis
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:uu:diva-500245DOI: 10.1109/TAI.2022.3168246Scopus ID: 2-s2.0-85151589461OAI: oai:DiVA.org:uu-500245DiVA, id: diva2:1750612
Note
Export Date: 13 April 2023; Article; 通讯地址: Lv, Z.; Uppsala University, Sweden; 电子邮件: lvzhihan@gmail.com; 基金资助详情: National Natural Science Foundation of China, NSFC, 61902203; 基金资助文本 1: This work was supported by the National Natural Science Foundation of China under Grant 61902203.; 参考文献: Ashton, K., That 'Internet of Things' thing (2009) RFID J., 22 (7), pp. 97-114; Atzori, L., Iera, A., Morabito, G., The Internet of Things: A survey (2010) Comput. Netw., 54 (15), pp. 2787-2805; Qiu, J., Du, L., Zhang, D., Su, S., Tian, Z., Nei-TTE: Intelligent traffic time estimation based on fine-grained time derivation of road segments for smart city (2020) IEEE Trans. Ind. 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