DataCite
metadata only
CC BY 4.0
Design of a Cost-Effective IoT-Edge AI Framework for Real-Time Landslide Prediction in Shimla, Himachal Pradesh
artificial-intelligenceearly-warning-systemedge-computinginternet-of-thingsiotnatural-hazardsreal-time-monitoring
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
- Design of a Cost-Effective IoT-Edge AI Framework for Real-Time Landslide Prediction in Shimla, Himachal Pradesh#20933
- Machine Learning-based Landslide Susceptibility Modeling in the Dibang Valley, NE India#443654
- Explainable AI (xAI) for Landslide Susceptibility Modeling: A Comparative Analysis of Machine Learning and Deep Learning Approaches#443646
- Prediction of changes in landslide rate induced by rainfalls: from the use of a black box model to a 1D mechanical model#417910
- In-Depth Analysis of Landslide Determinants and Susceptibility Assessment Using Machine Learning Models: Case Study of Bab Taza, Central Rif-Morocco#390092
Record · ID 20934
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