arXiv (All)
open access
Multimodal Deep Survival Analysis for Sinkhole Susceptibility
Sinkholes are a widespread geohazard in karst terrain. In Florida, soluble carbonate bedrock, shallow groundwater, and intense rainfall combine to make subsidence both common and spatially heterogeneous. Predicting where and when sinkholes will occur is difficult for two reasons. First, locations wi
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
- Debates—Stochastic subsurface hydrology from theory to practice: A geologic perspective#611956
- T374: Groundwater Flow and Contaminant Transport Modelling: Fundamentals an — Defra Commercial — Steven Thornton#656347
- T520: Fundamentals of Groundwater Modelling — Defra Commercial — Groundwater Science Ltd#656335
- Comparison of GRNN-MC and RF models for predicting soil hydrogeological and geotechnical profile using borehole data.#304956
- Machine Learning-based Landslide Susceptibility Modeling in the Dibang Valley, NE India#443654
Record · ID 1215468
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