#riskassessment — Public Fediverse posts on home.social

💧🌏 Greg Cocks @[email protected] · 2026-08-05 · 05:02 UTC

Flood And Landslide Susceptibility Assessment And Multi Hazard Interaction Mapping Using Machine Learning And GIS For Sustainable Settlement Planning In Nepal
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https://doi.org/10.1007/s44288-026-00670-8 <-- shared paper
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H/T @Narayan Thapa | Earth Data Modeling
“Nepal lies within an active seismic zone and is influenced by most dynamic climatic systems in the world. It faces compounding floods and landslide threats. Impacts are worst where multi-hazard interactions create spatially linked corridors. Despite frequent co-occurrence, national-scale assessments remain limited. This study presents machine learning and GIS-based approach to map nationwide susceptibility to floods, landslides, and identify their potential interaction zones, and delineate critical multi-hazard flow zones through spatial adjacency analysis. Using Google Earth Engine, the Random Forest model integrates topographic, climatic, environmental, and hydrological datasets to overcome subjective expert-driven methods. The model achieved strong predictive accuracy (AUC: 0.84 for floods, 0.85 for landslides). The results showed 19% of Nepal’s lowlands are medium to very highly susceptible to inundation, threatening approximately 900,000 people and over 3.4 million buildings; whilst in the hilly terrains, 40% is susceptible to slope-failure endangering 200,000 people and about 0.6 million buildings. K-means clustering followed by spatial adjacency analysis identified four spatial zonation: 81% of national area as low-hazard zone, 9% as flood-only zone, 5% as landslide-only zone, and 5% as interaction zones. Critical multi-hazard flow zone covering 7,588 km² represents spatially connected corridors linking interaction zones to downstream flood-prone populated areas, affecting 88 km² built-up land and 1,722 km² cropland. These zones represent susceptibility-based spatial connectivity rather than physically simulated cascading processes. These findings support recommendations for risk-informed land-use planning, resilient infrastructure development and climate adaptation aligned to sustainable development and investment risk screening…”
#GIS #spatial #mapping #GoogleEarthEngine #MachineLearning #RemoteSensing #GeospatialAI #DisasterRiskReduction #MultiHazard #ClimateAdaptation #climatechange #extremeweather #LandUsePlanning #SustainableDevelopment #InfrastructurePlanning #RiskAssessment #NaturalHazards #Nepal #EarthObservation #HinduKushHimalaya #HKH #HinduKush #Himalayas #risk #hazard #assessment #national #regional #spatialanalysis #spatiotemporal #massmovement #landslide #assessment #mitigation #water #hydrology #flood #flooding #sustainability

#gis #spatial #mapping #googleearthengine #machinelearning #remotesensing

ResearchBuzz: Firehose @[email protected] · 2026-07-30 · 15:24 UTC

University of Washington: 6.5 million Americans face landslide risks — a new database shows where they live. “The new Landslide Exposure Database is the first resource to map building-level landslide risk for the entire U.S. The database, developed by University of Washington researchers, rates 128 million buildings by landslide susceptibility, and uses socioeconomic data to assess […]

https://rbfirehose.com/2026/07/30/university-of-washington-6-5-million-americans-face-landslide-risks-a-new-database-shows-where-they-live/

#landslides #mapping #maps #naturaldisasters #publicsafety #riskassessment

💧🌏 Greg Cocks @[email protected] · 2026-06-17 · 05:25 UTC

Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
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https://doi.org/10.1038/s44304-026-00217-4 <-- shared paper
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https://doi.org/10.1038/s41598-025-22051-w <-- shared (earlier) paper
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https://doi.org/10.1080/2150704X.2025.2488532 <-- shared (earlier) paper
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H/T @abhinav Alangadan
“Can we develop a first-order understanding of permafrost degradation and glacial lakes exposed to degradation-induced mass movements in the Himalaya?
[The authors] tried to address this question. The study [first link above] integrates machine learning, statistical modeling, and numerical modeling to investigate high-resolution permafrost distribution, potential degradation, and associated mass-movement hazards in the Kinnaur district of Himachal Pradesh, India.
Using rock glaciers as proxies, [they] generated a high-resolution permafrost distribution using machine learning, while potential degradation zones were delineated using the 0°C isotherm as a first-order indicator. [They] further identified glacial lakes located near potentially degrading permafrost zones and reconstructed their bathymetry. A detailed scenario-based GLOF process-chain simulation was then carried out for Kashang Lake using r.avaflow and HEC-RAS.
[Their] results indicate that seven glacial lakes in #Kinnaur are located close to potentially degrading permafrost zones. The simulations further show that a potential GLOF from Kashang Lake could inundate critical downstream infrastructure, including nearly 11 km of National Highway 5…”
#permafrost #distribution #GIS #spatial #mapping #Himalayas #India #Kinnaur #HimachalPradesh #KashangLake #massmovement #engineeringgeology #machinelearning #AI #model #modeling #numericalmodel #glaciallakes #glaciet #glacial #glaciallakeoutburstflood #GLOF #cryosphere #geostatistics #rockglaciers #GeoAI #bathymetry #processchainsimulation #HECRAS #avaflow #risk #hazard #mitigation #riskassessment #infrastructure #HEP #publicsafety #downstream #avalanche

#kinnaur #permafrost #distribution #gis #spatial #mapping

Mx Verda @[email protected] · 2026-02-13 · 08:30 UTC

"Idk, I think it's less about empathy or compassion, and more about making sure the (hopefully small) group of uncaring people who enter medicine or allied health professions are incentivised to act pro-socially.
Like, in your region, what studying conditions and working conditions are clinicians expected to tolerate? Do they receive a living wage throughout study and work? Is their livelihood for a single person or big enough they can build a household?

Do they feel comfortable being a whistleblower when something goes wrong? (Numbers-wise, mistakes will happen over time.)
How robust is the inspection or regulation? How flexible are the logistics of each sector?
How many crises at once can be weathered without change to patient care? How long will it take to build back up that 'extra' capacity / contingency margin?

Who (in what position, in which teams) is responsible (or in what order) for noticing angels of death? What about anyone with less final but still harmful motives?

How severe are the consequences for failure? How many people feel compelled to hide errors rather than remedy them? What options for remediation are accessible to all staff, including contractors?"

#MedMastodon #DoLessHarm #SystemsThinking #HarmReduction #Medical #Medicine #MD #Systems #NonExpert #LayPerson #RubberNecking #Risk #RiskAssessment #RiskManagement #Risk_Management #Audit #Efficiency #Efficacy #Flexibility #TrueCrime #Crime