Raw Data for "STR-ConvLSTM: A Spatiotemporal Residual Learning Framework for Multivariate Salinity Forecasting in the Yangtze River Estuary"

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Authors: Yonggui Li, Ruyun Chen, Hao Chen, Zhiyan Che
Cited by: 0

Abstract

Dataset for "STR-ConvLSTM: A Spatiotemporal Residual Learning Framework for Multivariate Salinity Forecasting in the Yangtze River Estuary" (PeerJ submission). It includes spatiotemporal data (29.5°N–33.5°N, 121.0°E–123.0°E) for the Yangtze River Estuary: Sea Surface Salinity (SSS, primary predictand) and covariates (current velocities, SST, SSH). Sourced from CMEMS GLOBAL_MULTIYEAR_PHY_001_030 (1/12° spatial, daily temporal resolution), these raw/preprocessed data support STR-ConvLSTM model training/validation.

Fields of Study & Subjects

Hydrological Forecasting Using AI Oceanographic and Atmospheric Processes Model Reduction and Neural Networks

Publication details

Journal / source
Figshare
Publication date
1 January 2026
Document type
Dataset