SHAP
SHAP on World Data Ocean: a running collection of 2 stories we have gathered and hand-picked because they are worth your time. Every post here touches on shap in some way — the news, the analysis, the deep dives, and the occasional surprise find. A Global Hub for Ocean Intelligence 4 World Data Ocean is a centralized digital platform where researchers, scientists, and ocean enthusiasts converge to explore, analyze, and… New stories are added to this page as we find them, so check back if you want to keep up with what is happening around shap, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything World Data Ocean is covering right now.
Public-service exposure-oriented coastal flood susceptibility assessment and priority zone identification in the Pearl River Estuary
Rapid urbanization in estuarine lowlands intensifies coastal flood risk, impacting vital infrastructure and services. This study introduces an innovative, explainable machine-learning framework for coastal flood susceptibility (CFS) assessment and priority zone identification within the Pearl River Estuary. Utilizing flood records, environmental data, and exposure receptors, CatBoost modeling achieved a high AUC of 0.870, revealing extreme rainfall and wetland proximity as key drivers.
Coupling coordination between integrated transport–shipping system and nearshore marine ecology in China’s coastal cities: spatiotemporal evolution and XGBoost–SHAP-based mechanism identification
Achieving a dynamic equilibrium between integrated transport-shipping systems (ITS) and nearshore marine ecology (NME) is paramount for the sustainable development of China’s coastal cities. This study, utilizing longitudinal panel data from 53 cities (2013-2024), assesses the coupling coordination degree (CCD) between these systems. Employing the XGBoost–SHAP approach, we identify key influencing mechanisms, revealing that urbanization and trade dependence significantly shape CCD outcomes. For further exploration of related data-driven approaches, see our article, "Machine learning predictions for microbial eukaryotic plankton."