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FloatChat AI is introduced as a multimodal platform designed to enhance accessibility and utility of Argo float oceanographic data by addressing limitations in existing conversational interfaces. The platform incorporates several novel components, including a QC-Guard Layer for quality control, an Explainable AI module using SHAP, vision-language model integration, real-time anomaly detection with LSTM-Autoencoders, and predictive forecasting via Temporal Fusion Transformers. Evaluations using quantitative ML benchmarks and user studies demonstrate FloatChat AI's ability to improve data accessibility, reliability, and scientific rigor in oceanographic data intelligence.
Unlock oceanographic insights without coding: FloatChat AI brings explainable AI, anomaly detection, and predictive forecasting to complex Argo float data through natural language.
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.