HAL (France)
open access
Which strategy to improve the performances of an LSTM-based model for extreme stream temperature values?
Deep-learning models have demonstrated strong performances in reproducing stream temperature dynamics, which is promising for the reconstruction of missing stream temperature records at ungauged locations. However, model accuracy over the range of high, summer stream temperature values has been usua
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
- Assessing trends and forecasting meteorological drought in South Africa using Savitzky-Golay enhanced hybrid deep learning.#258450
- Benchmarking classical, ensemble, and deep learning models for soil temperature prediction: A multi-criteria evaluation approach, Türkiye#429528
- Enhanced Rainfall Forecasting Through Deep Learning Optimization Using Long Short-Term Memory Networks#1017859
- Learning Regional Snow Water Equivalent and Snow Height Variations from Sentinel-1 InSAR Acquisitions#1128876
- Identifying Arctic states vulnerable to rapid ice loss with a convolutional neural network#1328174
Record · ID 1312476
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