Work

Dynamically Tuned Variational Mode Decomposition and Convolutional Bidirectional Gated Recurrent Unit Algorithm for Coastal Sea Level Prediction

Year: 2025

Type: article

Abstract: This study proposes a hybrid sea level prediction model by coupling a dynamically optimized variational mode decomposition (VMD) with a convolutional bidirectional gated recurrent unit (CNN-BiGRU). The VMD decomposition is fine-tuned using the grey wolf optimizer and evaluated via entropy criteria to minimize mode mixing. The resulting components are processed by CNN-BiGRU to capture spatial features and temporal dependencies, and predictions are reconstructed from the integrated outputs. Validated on monthly sea level data from Kanmen and Zhapo stations, the model achieves high accuracy with an RMSE of 13.857 mm and 16.230 mm, MAE of 10.659 mm and 13.129 mm, and NSE of 0.986 and 0.980. With a 6-month time step, the proposed strategy effectively captures both periodic and trend signals, demonstrating strong dynamic tracking and error convergence. It significantly improves prediction accuracy and provides reliable support for storm surge warning and coastal management.

Source: Journal of Marine Science and Engineering

DOI: 10.3390/jmse13112055

Authors: Zhou Zhou, Gang Chen, Ping Zhou, Weibo Rao, Jifa Chen

Institutions: China University of Geosciences, Jiangxi University of Water Resources and Electric Power, Jiangxi Normal University

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $3,137