Tropical Cyclone Trajectory Forecasting System (LSTM & ONNX)
LSTM-based tropical cyclone trajectory prediction and visualization system evaluating 12 experimental scenarios (mean Haversine error of 14.2 km and R² of 0.999). The optimal model is converted to ONNX format for direct in-browser execution via ONNX Runtime on an interactive map built with Astro Island Architecture.
Frontend & Research Developer
Jan 1, 2026
Web Application
System Specifications & Context
Deep learning models for meteorological trajectory forecasting are predominantly restricted to offline computing environments, creating a barrier to interactive spatial visualization and accessible browser-based simulation.
Architecture & Implementation
Evaluated 12 empirical LSTM scenarios (achieving a 14.2 km mean Haversine error and 0.999 R²) and converted the optimal model to ONNX format, enabling direct client-side inference in the browser via ONNX Runtime within an Astro Island Architecture geospatial interface.
Technology Stack & Toolchain
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