#riskassessment — Public Fediverse posts

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

Hydroclimate Volatility On A Warming Earth
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https://doi.org/10.1038/s43017-024-00624-z <-- shared 2025 paper
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https://newsroom.ucla.edu/releases/floods-droughts-fires-hydroclimate-whiplash-speeding-up-globally <-- shared UCLA article, “Floods, Droughts, Then Fires: Hydroclimate Whiplash Is Speeding Up Globally “
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H/T @Daniel Swain
“Hydroclimate volatility refers to sudden, large and/or frequent transitions between very dry and very wet conditions. In this Review, we examine how hydroclimate volatility is anticipated to evolve with anthropogenic warming. Using a metric of ‘hydroclimate whiplash’ based on the Standardized Precipitation Evapotranspiration Index, global-averaged subseasonal (3-month) and interannual (12-month) whiplash have increased by 31–66% and 8–31%, respectively, since the mid-twentieth century. Further increases are anticipated with ongoing warming, including subseasonal increases of 113% and interannual increases of 52% over land areas with 3 °C of warming; these changes are largest at high latitudes and from northern Africa eastward into South Asia. Extensive evidence links these increases primarily to thermodynamics, namely the rising water-vapour-holding capacity and potential evaporative demand of the atmosphere. Increases in hydroclimate volatility will amplify hazards associated with rapid swings between wet and dry states (including flash floods, wildfires, landslides and disease outbreaks), and could accelerate a water management shift towards co-management of drought and flood risks. A clearer understanding of plausible future trajectories of hydroclimate volatility requires expanded focus on the response of atmospheric circulation to regional and global forcings, as well as land–ocean–atmosphere feedbacks, using large ensemble climate model simulations, storm-resolving high-resolution models and emerging machine learning methods…
#water #hydrology #hydroclimate #whiplash #global #spatialanalysis #spatiotemporal #weatherwhiplash #ecogeomorphology #sustainability #ecology# ###
#water #hydrology #hydroclimate #volatility #dry #wet #drought #flood #flooding #wildfire #landslide #massmovement #whiplash #global #spatialanalysis #spatiotemporal #weatherwhiplash #ecogeomorphology #sustainability #ecology #hydrogeomorphology #climatechange #extremeweather #anthropogenicwarming #climate #weather #connection #StandardizedPrecipitationEvapotranspiration #precipitation #rainfall #research #evapotranspiration #risk #hazard #riskassessment #disease #pandemic #publichealth #publicsafety #waterquality #watersecurity #watermanagement #hydrography #atmospheric #regional #global #forcing #climatemodel #model #modeling #AI #machinelearning

🚨 FEMA’s Hazus v7.2 Is Here — A Major Upgrade For Disaster Risk Modeling
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https://www.fema.gov/flood-maps/products-tools/hazus <-- shared link to FEMA HAZUS download, documentation, use case, etc
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[I used to work some with Hazus back in the back, but my career changed path; I still appreciate its strength and unity of purpose (sic) #alldataisspatial]
H/T @Laban "L.J." Johnson | Founder, LJ Learn & Concordia Initiative | Crisis Support · Leadership Development · Community Resilience | Bridging worlds to help people rise
“FEMA’s Hazus GIS platform has been updated with a new ArcGIS Pro–based version, bringing faster, more powerful tools for estimating losses from floods, hurricanes, earthquakes, and other natural hazards.
Key updates in Hazus 7.2 include:
• Streamlined workflows for flood and hurricane modeling
• New Earthquake ShakeMap integration using USGS data
• Expanded and improved results exports and reporting (including geodatabase outputs)
• Stronger security with known vulnerabilities addressed
• Performance improvements and optimized installation process
• [Significantly enhanced and comprehensive summary reports for flood and earthquake are now available for download.]
• Full integration with ArcGIS Pro (3.4–3.6) for a modern GIS experience
This release represents a significant step forward in how hazard planners, emergency managers, and GIS professionals analyze and prepare for disaster impacts…”
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“FEMA’s Hazus program provides software, data, methods, and guidance for estimating risk from natural hazards. Hazus can estimate building damages, economic losses, displaced households, casualties, debris generation and more resulting from a natural hazard event and can be used in all phases of emergency management…”
#HAZUS #fedservice #fedscience #oublicgood #publicsafety #emergencyresponse #software #spatialdata #GIS #spatial #mapping #risk #hazard #riskassessment #naturalhazard #humanimpacts #earthquake #wildfire #spatialanalysis #spatiotemporal #flood #flooding #cost #damage #economic #publicsafety #publichealth #emergencymanagement #opensource #opendata #tsunami #tornado #hurricane #ShakeMap #infrastructure #planning #policy #preparedness #impacts #geology #engineeringgeology #remotesensing #earthobservation
@FEMA

Frozen No More - A Case Study Of Arctic Permafrost Impacts Of Oil And Gas Withdrawal
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https://doi.org/10.1038/s41598-024-76292-2 <-- shared paper
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#GIS #spatial #mapping #remotesening #earthobservation #oil #gas #oilandgas #O&G #energy #extraction #drilling #exploration #subsurface #permafrost #melting #humanimpacts #climatechange #oilextraction #risk #hazard #riskassessment #water #hydrology #sediment #tundra #oilwellpads #NorthSlope #Alaska #arctic #spatialanalysis #spatiotemporal #remediation #drillpad #oilpad #lease #industry #habitat #ecosystem #fieldwork #sampling #geology #engineeringgeology #impact

New Study Links Flood Risk And Hurricane Attributes In Southwest Florida
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https://coastalscience.noaa.gov/news/new-study-links-flood-risk-and-hurricane-attributes-in-southwest-florida/ <-- shared technical article
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https://doi.org/10.1038/s44304-025-00141-z <-- shared paper
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#GIS #spatial #mapping #model #modeling #risk #hazard #building #damage #flood #flooding #hurricane #Florida #USA #economy #cost #infrastructure #extremeweather #HurricaneIrma #HurricaneIan #floodmodel #SLR #sealevelrise #stormsurge #vegetation #FEMA #NFIP #floodinsurance #HAZUS #opendata #stakeholders #riskassessment #propertyvalue
@NOAA