#riskassessment — Public Fediverse posts on home.social

Ivi Choc @[email protected] · 2026-08-25 · 16:44 UTC

#FYI #PaulBeckwith video lecture & literature review #ExtremeWeather #RiskAssessment

Paul about the necessity to account for #WorstCase scenarios

Supported by Sir Isaac Newton ESP*

https://youtu.be/5ORCfwEPGTQ

(*Emotional Support Puppy)

#climate #ClimateScience #climatechange #ClimateEmergency #ClimateCrisis #globalWarming #globalHeating #polycrisis

#fyi #paulbeckwith #extremeweather #riskassessment #worstcase #climate

Eric Maugendre on adapting @[email protected] · 2026-08-09 · 08:03 UTC

Society must anticipate: prepare not for this year's climate but for the future climates. 🧵

#futures #risks #risk #riskManagement #riskAssessment #livability #habitability #climatePolicy #policy #adaptation #plantations #forestry #climateDrift #climateChange #climateFailure #climateCollapse #drought #heatwaves #naturalDisasters #resilience #agriculture #megafires #wildfire #wildfires

#futures #risks #risk #riskmanagement #riskassessment #livability

Ivi Choc @[email protected] · 2026-08-08 · 20:08 UTC

RE: https://mastodon.social/@paulbeckwith/117055792281374330

#FYI #PaulBeckwith video lecture & literature review #ExtremeWeather #RiskAssessment

Beckwith, Paul Beckwith climate detective investigates the new Extreme Event Attribution Report

#climate #ClimateScience #climatechange #ClimateEmergency #ClimateCrisis #ClimateDisruption #globalWarming #globalHeating #polycrisis

#riskassessment #climate #climatescience #climatechange #climateemergency #climatecrisis

💧🌏 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

💧🌏 Greg Cocks @[email protected] · 2026-08-05 · 04:54 UTC

Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Techniques in the Marand Plain [Iran]
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https://doi.org/10.3390/geosciences15030110 <-- shared paper
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H/T @Geosciences MDPI
“This study applies advanced machine learning algorithms to map flood susceptibility in northwest Iran. The results demonstrate strong predictive performance, with the Locally Weighted Linear model delivering the highest accuracy and providing valuable guidance for flood-risk management and disaster mitigation…”
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“Flooding is one of the most significant natural hazards in Iran, primarily due to the country’s arid and semi-arid climate, irregular rainfall patterns, and substantial changes in watershed conditions. These factors combine to make floods a frequent cause of disasters. In this case study, flood susceptibility patterns in the Marand Plain, located in the East Azerbaijan Province in northwest Iran, were analyzed using five machine learning (ML) algorithms: M5P model tree, Random SubSpace (RSS), Random Forest (RF), Bagging, and Locally Weighted Linear (LWL). The modeling process incorporated twelve meteorological, hydrological, and geographical factors affecting floods at 485 identified flood-prone points. The data were analyzed using a geographic information system, with the dataset divided into 70% for training and 30% for testing to build and validate the models. An information gain ratio and multicollinearity analysis were employed to assess the influence of various factors on flood occurrence, and flood-related variables were classified using quantile classification. The frequency ratio method was used to evaluate the significance of each factor. Model performance was evaluated using statistical measures, including the Receiver Operating Characteristic (ROC) curve. All models demonstrated robust performance, with an area under the ROC curve (AUROC) exceeding 0.90. Among the models, the LWL algorithm delivered the most accurate predictions, followed by RF, M5P, Bagging, and RSS. The LWL-generated flood susceptibility map classified 9.79% of the study area as highly susceptible to flooding, 20.73% as high, 38.51% as moderate, 29.23% as low, and 1.74% as very low. The findings of this research provide valuable insights for government agencies, local authorities, and policymakers in designing strategies to mitigate flood-related risks. This study offers a practical framework for reducing the impact of future floods through informed decision-making and risk management strategies…”
#FloodSusceptibility #FloodRisk #MachineLearning #GIS #NaturalHazards #DisasterManagement #FloodModeling #Hydrology #EnvironmentalMonitoring #RiskAssessment #GeospatialAnalysis #ClimateResilience #GIS #spatial #mapping #Iran #MarandPlain #EastAzerbaijan #machinelearning #AI #floodhazard #floodvulnerability #flood #flooding #water #hydrography #hydrology #model #modeling #risk #hazard #rainfall #precipitation #extremeweather #spatialanalysis #spatiotemporal #modelperformance #policy #planning #mitigation #design #riskmanagement

#floodsusceptibility #floodrisk #machinelearning #gis #naturalhazards #disastermanagement

💧🌏 Greg Cocks @[email protected] · 2026-04-23 · 21:52 UTC

Hanging Glaciers In Himalaya Reveal Rising Avalanche Risk
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https://www.nature.com/articles/d44151-026-00072-2 <-- shared technical article
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https://doi.org/10.1038/s44304-026-00205-8 <-- shared paper
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#GIS #spatial #mapping #risk #hazard #monitoring #engineeringgeology #naturalhazard #Himalaya #Himalayas #Alaknanda #basin #Garhwal #India #ice #cryosphere #massmovement #avalanche #glacier #hangingglacier #cryosphere #remotesensing #spatialanalysis #spatiotemporal #sentinel2 #DEM #elevation #model #modeling #GlabTop2 #unstable #hanging #BadrinathMana #impact #infrastructure #damage #HEP #urbanisation #development #population #publicsafety #demographics #glacialretreat #melting #Chamoli #disaster #earlywarningsystems #Himalayan #glaciers #instability #warming #climatechange #riskassessment #riskclassification #framework #avaflow #runout #downstream #downslope #water #hydrology #planning #policy #mitigation #geomorphology #geomorphometry

#gis #spatial #mapping #risk #hazard #monitoring

The DefendOps Diaries @[email protected] · 2025-10-07 · 14:57 UTC

Imagine your security team reacting in minutes instead of days—even against emerging threats like "BlackFrost." AI-powered breach and attack simulation is turning cyber defense into a proactive edge in 2025. Curious how it's reshaping the playbook?

https://thedefendopsdiaries.com/how-ai-powered-breach-and-attack-simulation-is-changing-cybersecurity-in-2025/

#aiincybersecurity
#breachandsimulation
#cybersecuritytrends
#threatintelligence
#riskassessment