CC-GRMAS: A Multi-Agent Graph Neural System for Spatiotemporal Landslide Risk Assessment in High Mountain Asia

CC-GRMAS: A Multi-Agent Graph Neural System for Spatiotemporal Landslide Risk Assessment in High Mountain Asia | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 23 October 2025 V1 Latest version Share on CC-GRMAS: A Multi-Agent Graph Neural System for Spatiotemporal Landslide Risk Assessment in High Mountain Asia Authors : Mihir Panchal 0009-0001-6459-0439 [email protected] , Ying-Jung Chen , and Surya Parkash Authors Info & Affiliations https://doi.org/10.22541/au.176124697.72632598/v1 104 views 110 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Landslides are a growing climate induced hazard with severe environmental and human consequences, particularly in high mountain Asia. Despite increasing access to satellite and temporal datasets, timely detection and disaster response remain underdeveloped and fragmented. This work introduces CC-GRMAS, a framework leveraging a series of satellite observations and environmental signals to enhance the accuracy of landslide forecasting. The system is structured around three interlinked agents Prediction, Planning, and Execution, which collaboratively enable real time situational awareness, response planning, and intervention. By incorporating local environmental factors and operationalizing multi agent coordination, this approach offers a scalable and proactive solution for climate resilient disaster preparedness across vulnerable mountainous terrains. Supplementary Material File (tccml_neurips_2025.pdf) Download 598.42 KB Information & Authors Information Version history V1 Version 1 23 October 2025 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords disaster management and relief earth observations and monitoring generative modeling natural language processing recommender systems Authors Affiliations Mihir Panchal 0009-0001-6459-0439 [email protected] Department of Computer Engineering Dwarkadas, Jivanlal Sanghvi College of Engineering Mumbai View all articles by this author Ying-Jung Chen College of Computing Georgia Institute of Technology Atlanta View all articles by this author Surya Parkash Geo-Hydro Meteorological Risks Management Division National Institute of Disaster Management Delhi View all articles by this author Metrics & Citations Metrics Article Usage 104 views 110 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Mihir Panchal, Ying-Jung Chen, Surya Parkash. CC-GRMAS: A Multi-Agent Graph Neural System for Spatiotemporal Landslide Risk Assessment in High Mountain Asia. Authorea . 23 October 2025. DOI: https://doi.org/10.22541/au.176124697.72632598/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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