FloatChat - An Explainable Multimodal AI Platform for Predictive Oceanographic Data Intelligence with QC-Aware Natural Language Querying

The Argo float program generates one of the most comprehensive global ocean observation datasets, yet its accessibility remains severely limited due to the technical complexity of data formats, quality-control procedures, and the programming

expertise required for meaningful analysis. While existing conver-sational interfaces for oceanographic data provide basic

natural language querying capabilities, they critically lack explainability, predictive intelligence, proactive anomaly detection,

and rigor-ous quality-control awareness — significantly restricting their scientific utility. This paper presents FloatChat AI, an

enhanced multimodal AI platform that addresses these limitations through six novel contributions: a QC-Guard Layer that

automatically injects Argo-compliant quality-control constraints into generated SQL queries, an Explainable AI response

module leveraging SHAP-based reasoning traces for non-expert interpretability, a Vision-Language Model integration

enabling image-to-query multimodal input, a real-time anomaly detection microservice employing LSTM-Autoencoders for

proactive ocean event alert-ing, a Temporal Fusion Transformer-based predictive forecasting engine for short-term

oceanographic trend prediction, and a collaborative multi-user workspace supporting shared annotation and synchronized

visualization. The system is evaluated through quantitative ML benchmarks including RMSE-based forecast accuracy, anomaly

detection precision-recall metrics, and QC-injection error reduction rates, supplemented by a user compre-hension study

comparing expert and non-expert interpretability outcomes. Experimental results demonstrate that FloatChat AI significantly

advances the state of accessible, reliable, and scien-tifically rigorous oceanographic data intelligence.