HAL (France)
open access
Assessing Urban Flood Susceptibility Using Random Forest Machine Learning and Geospatial Technologies: Application to the Bonoumin-Palmeraie Watershed, Abidjan (Côte d’Ivoire)
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
- Development of a Hydrodynamic-Simulation-Informed Random Forest Method for Efficient Spatiotemporal Flood Prediction#285400
- Replication Package - Physics-Informed Machine Learning for Extreme Rainfall Nowcasting: A Rapid Review Toward Sustainable Early Warning Systems#212907
- Modelado predictivo de la ocurrencia de focos de incendios forestales en Colombia mediante técnicas de machine learning#327758
- Advancing Environmental Risk Assessment Through Hydro-Climatological Modeling, Geospatial Analytics, and Machine Learning#446489
- Why forests can mitigate floods of all sizes: Evaluating the scientific basis for forest-based flood mitigation#257191
Record · ID 337653
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