Pearl River Hydraulic Research Institute

Deep learning detection of submesoscale eddies from MODIS SST in the South China Sea

This dataset provides daily records of submesoscale eddies in the South China Sea from 2015 to 2024, identified from MODIS Aqua and Terra sea surface temperature (SST) imagery. Distributed as daily TXT files, each record includes eddy polarity, center coordinates (longitude and l...

Risk Assessment of Shield Tunnelling in Mixed Ground Using a Coupled Subjective–Objective Weighting and Fuzzy Set Pair Analysis Model

Shield tunnelling in upper-soft and lower-hard mixed ground is characterised by strong geological heterogeneity and coupled construction risks, including uneven cutterhead loading, face instability, cutter wear and abnormal fluctuations in tunnelling parameters. This study assess...

IceRWKV: A Novel River Ice Segmentation Network Based on Context Clustering RWKV

River ice semantic segmentation is a crucial task that provides essential information for hydrological monitoring and infrastructure protection in cold regions. Previous works mainly focus on global long-range dependency modeling or local feature extraction, while the balance bet...