ULRR
metadata only
Uncertainty-aware forecasting of economic and socio-economic inputs for environmental and sustainability indicator assessment
Machine learning and statistical models that forecast environmental and sustainability indicators routinely use economic and socio-economic variables as inputs, yet the field offers no systematic guidance for forecasting these covariates when genuine forward-looking predictions are required. Treatin
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
- Advancing Environmental Risk Assessment Through Hydro-Climatological Modeling, Geospatial Analytics, and Machine Learning#446489
- Machine learning for research on climate change adaptation policy integration: an exploratory UK case study#8834
- Ozone Trends and Mortality Risk: The Growing Need for Machine Learning Predictions in Bogotá, Colombia#443649
- Learning Hierarchical Causal Representations of the Effects of Forcings on Temperature in Climate Models#1105554
- Database of Remote Sensing and Machine Learning-Based Key Performance Indicators for Monitoring Agricultural Water Management Efficiency in Climate Change Adaptation Across Africa#346711
Record · ID 977434
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