OpenMETU
metadata only
Flood early warning framework based on LSTM discharge forecasting
Accurate and timely flood forecasting remains a critical challenge for disaster risk reduction in urbanizing watersheds. This study presents an integrated flood forecasting system that couples Long Short-Term Memory (LSTM) neural network-based discharge forecasting with two-dimensional hydrodynamic
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A Novel Hybrid Dual-Stream Deep Learning Architecture Integrating Multi-State LSTM, CNN, and Multi-Head Self-Attention for Water Level and Discharge Prediction (Missouri River Basin, USA)#1081850
- A Hybrid CNN-LSTM Surrogate Model for Hyper-Resolution Spatiotemporal Flood Forecasting in Norfolk, Virginia#169475
- A Multi-Timestep LSTM Ensemble regressor for Enhanced Short-Term Runoff Prediction#1062144
- A Hybrid LSTM--Vision Transformer Architecture for Predicting HRRR Forecast Errors#287179
- Research on comprehensive drought index prediction model based on CNN-LSTM.#610414
Record · ID 1067478
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive