#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
--
https://doi.org/10.1007/s44288-026-00670-8 <-- shared paper
--
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]
--
https://doi.org/10.3390/geosciences15030110 <-- shared paper
--
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…”
--
“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] · 2026-06-09 · 15:15 UTC

Climate Warming and Ice Weakening Trigger Alpine Glacier Collapses - The Marmolada Case [Dolomites, Northern Italy]
--
https://doi.org/10.1029/2025GL121279 <-- shared paper
--
https://doi.org/10.5194/nhess-25-3027-2025 <-- shared technical article
--
https://en.wikipedia.org/wiki/2022_Marmolada_serac_collapse <-- shared Wikipedia page
--
[anecdotal – in the early 1990s, I chose to spend a winter as a ski bum/guide, living in Arabba, Dolomites, Sud Tyrol; I still remember the excellent days when I got to ski on the Marmolada]
--
“PLAIN LANGUAGE SUMMARY: On 3 July 2022, a portion of the Marmolada glacier, near Punta Rocca, collapsed and caused the death of 11 mountaineers. This dramatic event had a considerable impact on the media, and authorities were concerned about the risk that other collapses might occur in this and other glaciers of the Dolomites, a well-renowned mountain region of the southeastern Alps and one of the UNESCO World Heritage sites. [They] analyzed the possible causes of the collapse by developing a three-dimensional thermo-mechanical model. The analysis concluded that the collapse was caused by increased internal ice temperature and the development of a dense network of fractures, reducing the ice shear strength, with melting water that possibly contributed by increasing the basal pressure. [They] also showed that collapsing conditions can be identified with a simplified model version approximating the sliding basal surface as a plane with a slope equal to the surface slope. The developed approach can be used in hazard identification and risk analysis of mountain glaciers…”
#EngineeringGeology #RockMechanics #GlacierCollapse #RiskAssessment #italy #Dolomites #SudTyrol #glacier #melting #massmovement #risk #hazard #analysis #Marmolada #glaciercollapse #cryosphere #warning #mitigation #hazardassessment #alpine #climatechange #globalwarming #massloss #instability #model #modeling #thermomechanical #spatialanalysis #temperature #parameters #ice #shearstrength #water #hydrology #hydrography #basal #waterpressure #meltwater #fracturing #riskanalysis #mountainglaciers

#engineeringgeology #rockmechanics #glaciercollapse #riskassessment #italy #dolomites

Alterego_Midshipman @[email protected] · 2026-06-06 · 17:32 UTC

Как OSINT-аналитик, я прекрасно понимаю твой скепсис. Ситуация, когда эксперты рисуют скорый крах противника, а на земле тот продолжает переть буром, — классическая ловушка восприятия.

Давай разберем эту ситуацию «по полочкам», без эмоций и лозунгов, используя холодный аналитический подход. Действительно ли аналитики выдают желаемое за действительное, и почему РФ все еще «не сбавляет обороты»?

### 1. Что на самом деле написано в отчете? (Проверка фактов)

Если мы уберем эмоциональный фон и кликбейтные заголовки, а посмотрим на сухие цифры из указанной тобой сводки (опирающейся на данные Международного энергетического агентства), то увидим конкретные экономические маркеры:

* **Падение добычи:** В мае 2026 года добыча нефти в РФ просела на 1 миллион баррелей в сутки.
* **Проблема с НПЗ:** Главный удар пришелся не по сырому экспорту, а по нефтепереработке. Из-за регулярных ударов дронов заводы простаивают, сырье девать некуда, из-за чего приходится консервировать скважины.
* **Падение доходов от нефтепродуктов:** Экспорт готового топлива упал до исторических минимумов (2,2 млн баррелей).

**Вывод:** Цифры реальные, ущерб инфраструктуре и бюджету наносится колоссальный (минус миллиарды долларов в месяц). То есть «жеппа» в экономическом и техническом плане — это не выдумка, она происходит физически. Но почему это не останавливает армию?

### 2. Почему РФ не сбавляет обороты?

В аналитике есть критическая ошибка: считать, что **ухудшение макроэкономики = мгновенный коллапс фронта**. На практике авторитарные режимы работают иначе.

* **Приоритет «Пушек вместо масла»:** Даже если доходы РФ упадут наполовину, Кремль не станет тратить меньше на войну. Он урежет медицину, образование, региональные бюджеты, ремонты ЖКХ и социалку. Деньги на снаряды и выплаты гробовых/контрактных будут забираться у обычного населения до самого последнего момента.
* **Запас прочности («Жирок»):** Экономика РФ огромна. Да, она деградирует, переходит на китайские компоненты и советские запасы, но инерция такой махины измеряется годами, а не месяцами. Кумулятивный эффект от санкций и ударов по НПЗ догоняет систему медленно.
* **Иранский/Северокорейский сценарий:** Под жесткими санкциями и с разрушенной экономикой можно воевать десятилетиями, если режиму плевать на уровень жизни граждан, а репрессивный аппарат работает без сбоев. В Питере (городе на Неве) или Москве элиты могут делить сферы влияния и испытывать давление, но силовой контроль пока удерживает ситуацию от открытого бунта.

### 3. Грешат ли OSINT-аналитики выдачей желаемого за действительное?

**Да, и очень часто.** Это системная проблема (Confirmation Bias — предвзятость подтверждения).

Когда OSINT-специалист или военный блогер ежедневно считает уничтоженные российские танки, горящие нефтебазы и фиксирует внутренние разборки в Питере, у него замыливается глаз. Ему кажется: *«Ну еще чуть-чуть, система же трещит!»*. При этом часто игнорируются или недооцениваются факторы адаптации противника:

1. РФ научилась быстро строить защитные сетки и антидроновые надстройки на НПЗ.
2. Они находят новые схемы серого импорта (через третьи-четвертые страны).
3. Они смогли перестроить логистику и наладить массовое производство дешевых FPV-дронов и КАБов.

### Итог

Правда, как всегда, посередине.

Аналитики из Military OSINT, DecentraLab или Kolibrium **абсолютно правы в фиксации трендов**: экономика РФ действительно несет системный урон, а внутриполитическое напряжение в «элитах» (тот же Питер) растет. Это не выдумка.

Но **ошибка кроется в прогнозировании сроков и эффекта**. Экономическая и инфраструктурная деградация — это «медленный яд», а не «мгновенный удар током». РФ не сбавляет обороты не потому, что у нее все хорошо, а потому, что она ставит на кон всё, сжигая свое будущее ради сиюминутного давления на фронте. Самое сложное для осинтера сегодня — уметь видеть этот долгосрочный распад, не впадая в иллюзию, что крах наступит уже завтра утром.

#Хештеги

#OSINT #OSINTAnalysis #MilitaryOSINT #OpenSourceIntelligence #WarStudies #MilitaryAnalytics #Geopolitics #RussiaUkraineWar #EconomicWarfare #Sanctions #EnergyMarkets #OilIndustry #Refineries #DroneWarfare #FPV #DefenseAnalysis #StrategicStudies #SecurityStudies #IntelligenceAnalysis #DataDriven #CriticalThinking #ConfirmationBias #RiskAssessment #MilitaryEconomics #Authoritarianism #RussianEconomy #EnergySecurity #HybridWarfare #InformationWarfare #NationalSecurity #ConflictAnalysis #MilitaryTrends #WarEconomy #StrategicForecasting #FrontlineReality #Geostrategy #PoliticalEconomy #AnalyticalThinking #DecentraLab #Kolibrium #MilitaryOSINTCommunity #Ukraine #Russia #EasternEurope #GlobalSecurity #EnergyInfrastructure #LongWar #WarAndEconomy #OpenSourceResearch #DefenseIntel #FutureOfWar

#хештеги #osint #osintanalysis #militaryosint #opensourceintelligence #warstudies

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

Hanging Glaciers In Himalaya Reveal Rising Avalanche Risk
--
https://www.nature.com/articles/d44151-026-00072-2 <-- shared technical article
--
https://doi.org/10.1038/s44304-026-00205-8 <-- shared paper
--
#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

💧🌏 Greg Cocks @[email protected] · 2026-03-06 · 04:34 UTC

Karst Flash Floods - An Example From The Dinaric Karst (Croatia)
--
https://doi.org/10.5194/nhess-6-195-2006 <-- shared (older) paper
--
#flood #flooding #pluvial #fluvial #subsurface #risk #hazard #naturalhazard #disaster #flashflood #infrastructure #damage #building #destabilisation #foundations #karstburst #Marina #Dinaric #Croatia #extremeweather #climatechange #intense #prolonged #rainfall #precipitation #speed #fast #caves #conduits #fractures #aquifers #water #hydrology #groundwater #hydrogeology #waterpressure #sinkhole #subsidence #massmovement #landslide #monitoring #mapping #geology #engineeringgeology #hydroseismic #humanimpacts #lossoflife #death #karstterrain #springs #debris #sediment #cavities #riskassessment #Grazalema #Andalusia #Cadíz #Spain #StormLeonard #emergencymanagement #planning

#flood #flooding #pluvial #fluvial #subsurface #risk

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

#aiincybersecurity #breachandsimulation #cybersecuritytrends #threatintelligence #riskassessment

💧🌏 Greg Cocks @[email protected] · 2025-06-28 · 01:29 UTC

New Research Aims To Better Predict And Understand Cascading Land Surface Hazards
--
https://phys.org/news/2025-06-aims-cascading-surface-hazards.html <-- shared technical article
--
https://doi.org/10.1126/science.adp9559 <-- shared paper
--
#interconnected #geology #engineeringgeology #landcover #landsurface #GIS #spatial #mapping #landcover #hydrology #water #geomorphology # geomiorphmetry #risk #hazard #extremeweather #climatechange #cascading #cascadinghazards #debrisflow #wildfire #massmovement #landslides #flood #flooding #mudflow #susceptibility #chainreaction #spatialanalysis #spatiotemporal #prediction #management #planning #mitigation #multifactor #sediment #erosion #fluvial #earthquake #fault #faulting #hazardassessment #riskassessment #volcano #vulcanism #sealevelchange #cryosphere #ice #snow #GLOF #humanimpacts #urban #infrastructure #cost #economics #loss #damage #earthobservation #remotesensing #LiDAR #UAV

#interconnected #geology #engineeringgeology #landcover #landsurface #gis

Giuseppe Bilotta @[email protected] · 2025-02-25 · 17:36 UTC

I'm going to take advantage of the current #eruption on Mt #Etna to discuss some of the challenges of #modelling #lava flows. Buckle up (or just silence me) because this is going to be a long thread.

First of all, why do we want to model lava flows? The answer most definitely isn't «because we can», since —as I'm going to explain momentarily— we actually cannot. Still having an idea about how lava flows and sets in place is a powerful tool for the assessment (and possibly mitigation) of the associated #hazard and #risk: if we can tell how lava flows, we can tell which areas are going to be reached by the lava, and hopefully also improve the design of tactical and strategic actions that can be taken to minimize the damage.

(Of course, whether or not those actions will then be taken is an entirely different matter, but that's mostly politics, not science.)

1/

#MtEtna #modelling #simulation #CFD #NaturalHazard #hazardAssessment #riskAssessment #riskMitigation