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Toward Cost-Effective and Interpretable Sea-Level Prediction: MEMS Sensors and Temporal Fusion Transformer

Xueyu Ren,Jiawang Chen,Qixiao Zhou,Hai Zhu,Shang Sun,Y. Ge,C. Cao,Peng Zhou,Yulin Si,Yiyuan Zhang

Published 2026 in IEEE Transactions on Instrumentation and Measurement

ABSTRACT

Accurate sea-level prediction is crucial for coastal communities facing rising sea levels and increasing storm-surge threats. However, the black-box nature of existing predictive models limits their acceptance in operational decision-making contexts, creating an urgent need for transparent, cost-effective, and reliable forecasting solutions. Here, we propose a novel framework that combines micro-electromechanical systems (MEMS)-based ocean sensors with an interpretable machine learning (ML) approach to address this need. Utilizing two months of data collected from an MEMS accelerometer array on submarine cables, our approach integrates variational mode decomposition (VMD) to isolate key temporal patterns from raw sensor measurement data, and principal component analysis (PCA) to optimize feature selection. These refined features with meteorological and calendar information are fed into an interpretable ML model, the temporal fusion Transformer (TFT), for sea-level prediction. The TFT provides interpretable outputs, including the importance ranking of input variables and attention distribution of different time steps. Our hybrid VMD–PCA–TFT model achieved a root-mean-square error (RMSE) of 1.57 cm and a coefficient of determination ( $R^{2}$ ) of 0.98 compared with tide gauge records, outperforming existing models. These results demonstrate that MEMS-based ocean measurement systems can match the accuracy of traditional methods at a fraction of the cost. Moreover, the TFT’s interpretable analysis reveals that the VMD–PCA features accounted for more than 80% predictive power, providing transparent insights into prediction mechanisms. This dual advantage of cost-effectiveness and interpertability of our framework could potentially promote widespread deployment of ocean monitoring systems and advance prediction capabilities for climate change adaptation.

PUBLICATION RECORD

  • Publication year

    2026

  • Venue

    IEEE Transactions on Instrumentation and Measurement

  • Publication date

    Unknown publication date

  • Fields of study

    Not labeled

  • Identifiers
    DOI 10.1109/TIM.2025.3632438
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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