NCBI PubMed Central
open access
Data-driven climatic zoning and future trend forecasting in Chad using artificial neural networks.
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
- Benchmarking classical, ensemble, and deep learning models for soil temperature prediction: A multi-criteria evaluation approach, Türkiye#429528
- The NCEP Climate Forecast System Version 2#7557
- Machine learning techniques for forecasting the effect of climate change and anthropogenic pressures on coastal wetlands (Ichkeul Lake, Ramsar site)#334341
- Design and Comparison of Parallel Dynamic Matérn Kernel-Based Regression Models and Machine Learning Approaches: Application to Bias Correction in Numerical Weather Prediction#150708
- Forecasting Global Rainfall in a Changing Climate: A Machine Learning Approach Using Köppen-Geiger Zones#339211
Record · ID 305415
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