#floodmodeling — Public Fediverse posts on home.social

💧🌏 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] · 2026-07-02 · 19:26 UTC

Flood Vulnerability And Load Capacity Assessment Of Historic Masonry Arch Bridges In Ireland Under Changing Climates
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https://doi.org/10.1016/j.eve.2026.100153 <-- shared paper
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H/T @Upaka Rathnayake
“HIGHLIGHTS:
• Field surveys and flood modelling were used to evaluate the resilience of eight historic masonry arch bridges in County Offaly, Ireland, under projected fluvial flooding conditions.
• Results indicate that increased flood levels and hydraulic forces can significantly reduce bridge load-carrying capacity, with potential reductions of up to 40% due to buoyancy effects during extreme flood events.
• The study demonstrates a clear relationship between flood exposure and structural deterioration, emphasizing the need for structural health monitoring, maintenance strategies, and climate-resilient infrastructure management…
ABSTRACT: Masonry bridges, predominantly constructed from stone or brick, were a common feature of bridge engineering during the 18th and 19th centuries. However, these historic bridges are still in use today, but they are at risk due to various extreme climate conditions. Thus, these bridges are vulnerable to damage and needy for investigation. This paper offers an in-depth analysis of the projected impacts of fluvial flooding stemming from climate change on a number of masonry arch bridge structures located in county Offaly, Ireland. It evaluates bridge resilience by examining estimated flood levels alongside the overall condition of the structures. These assessments play a crucial role in determining the load-bearing capacity of the bridges and whether adjustment factors should be implemented. Particularly for bridges situated on primary and secondary roads with consistent heavy goods vehicle (HGV) traffic, the potential decrease in load-bearing capabilities warrants significant consideration. This study highlights concerns regarding the resilience of these historic structures and presents a valid argument regarding their suitability for contemporary environmental conditions and present-day activities…”
#bridge #resilience #climatechange #impacts #loadcarrying #capacity #masonry #archbridges #fluvial #flood #flooding #Ireland #casestudies #transportation #bridges #historicbridges #history #survey #model #modeling #floodmodeling #CountyOffaly #ContaeUíbhFhailí #hydraulics #engineering #chokepoint #constraint #constriction #hydraulicforce #bridgeload #carryingcapacity #buoyancyeffects #damage #structuraldeterioration #structuralhealth #monitoring #maintenance #planning #policy #climateresilience #infrastructure #management #water #hydrography