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ORIGINAL RESEARCH article

Front. Mar. Sci.
Sec. Ocean Observation
Volume 11 - 2024 | doi: 10.3389/fmars.2024.1472047

Enhancing typhoon wave hindcasting with Random Forests and BP Neural Networks in the SWAN model

Provisionally accepted
Cheng Chen Cheng Chen 1*Hongkun Lin Hongkun Lin 1Dawei Guan Dawei Guan 2Feng Cai Feng Cai 3Qiaoyi Wang Qiaoyi Wang 4Qingchun Liu Qingchun Liu 4
  • 1 College of Civil Engineering, Fuzhou University, Fuzhou, China
  • 2 College of Harbour, Coastal and Offshore Engineering, Hohai University, Nanjing, Liaoning Province, China
  • 3 Third Institute of Oceanography, State Oceanic Administration, Xiamen, Fujian Province, China
  • 4 Fujian Lugang (Group) Co.Ltd.,Quanzhou 362000,China, Quanzhou, China

The final, formatted version of the article will be published soon.

    In this paper, the numerical wave model SWAN was enhanced through integration with two machine learning methods: the Back Propagation Neural Network and Random Forest. This integration facilitated the development of two distinct models, namely SWAN-BP and SWAN-Tree. Through correlation analysis, key input features were identified for the machine learning models. The forecasts from the SWAN model were subsequently utilized as inputs to enhance further wave prediction. These hybrid models were validated using data from Typhoon Doksuri (2023) and Typhoon Nesat (2017). The results indicated significant improvements in predicting typhoon-induced wave heights with both the SWAN-BP and SWAN-Tree models compared to the original SWAN model. Specifically, the SWAN-BP model demonstrated a 33% improvement in accuracy for the Typhoon Doksuri, whereas the SWAN-Tree model exhibited a 24% improvement. For Typhoon Nesat, the accuracy improvements were 23% for the SWAN-BP model and 21% for the SWAN-Tree model. These findings demonstrate that integrating wave numerical models with machine learning techniques can significantly enhance the predictive accuracy of numerical models. This approach offers a cost-effective means to improve the existing wave forecasting database.Traditionally, the direct use of meteorological and oceanographic data for typhoon wave prediction might be compromised by biases inherent in the numerical wave models.However, the SWAN-BP and SWAN-Tree models effectively reduce these biases, thereby providing more accurate and robust predictions. In conclusion, this paper enhances the predictive accuracy of the SWAN model and establishes a crucial foundation for more precise typhoon wave forecasting through the application of machine learning techniques.

    Keywords: Typhoon waves, SWAN model, machine learning, back propagation neural network, random forest, optimization

    Received: 28 Jul 2024; Accepted: 03 Sep 2024.

    Copyright: © 2024 Chen, Lin, Guan, Cai, Wang and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Cheng Chen, College of Civil Engineering, Fuzhou University, Fuzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.