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#floodhazard — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #floodhazard, aggregated by home.social.

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  1. 💧🌏 Greg Cocks @[email protected] ·

    Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Techniques in the Marand Plain [Iran]
    --
    doi.org/10.3390/geosciences150 <-- 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
  2. 💧🌏 Greg Cocks @GregCocks ·

    Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Techniques in the Marand Plain [Iran]
    --
    doi.org/10.3390/geosciences150 <-- 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
  3. 💧🌏 Greg Cocks @[email protected] ·

    [Open] Data Related To Flood Mapping [Canada]
    --
    natural-resources.canada.ca/sc <-- shared link to technical details
    --
    app.geo.ca/en-ca/map-browser/r <-- shared map/data-portal link, Canada Flood Map Inventory (CFM)
    --
    open.canada.ca/data/en/dataset <-- shared data-portal link, Canada Flood Susceptibility Index
    --
    doi.org/10.3390/ECWS-7-14235 <-- shared (2023) paper
    --
    doi.org/10.1002/2017WR020917 <-- shared (2017) paper
    --
    H/T @Michael DePue | VP & AtkinsRéalis Fellow for Water Resources Engineering | PE, PMP, CFM
    “At the Canadian Water Resources Association National Conference in Winnipeg, colleagues shared insights from Canada's Flood Hazard Identification and Mapping Program. This initiative has seen over 400 flood mapping projects and more than 1,000 flood hazard maps produced, supported by a substantial investment of $164.2 million from 2024 to 2028.
    Two key datasets:
    • The Canada Flood Map Inventory, which records the locations of flood hazard maps and provides information on how to access them.
    • The national Flood Susceptibility Index, a machine-learning assessment of flood-prone areas, including regions that have not been mapped in detail.
    When these two layers are combined on a single screen, it becomes clear where future mapping efforts should be directed — specifically, areas with high susceptibility that currently lack detailed maps…”
    #water #hydrography #flood #flooding #risk #hazard #model #modeling #fedscience #publicsafety #humaninpacts #opendata #Canada #GIS #spatial #mapping #damage #infrastructure #floodmapping #prediction #spatialanalysis #spatiotemporal #historic #current #future #preduction #extremeweather #metrology #rainfall #precipitation #atmosphericriver #FloodMapInventory #CFM #floodhazard #FloodSusceptibilityIndex #floodprone #research #susceptibility
    @NRCAN

    #water #hydrography #flood #flooding #risk #hazard
  4. 💧🌏 Greg Cocks @GregCocks ·

    [Open] Data Related To Flood Mapping [Canada]
    --
    natural-resources.canada.ca/sc <-- shared link to technical details
    --
    app.geo.ca/en-ca/map-browser/r <-- shared map/data-portal link, Canada Flood Map Inventory (CFM)
    --
    open.canada.ca/data/en/dataset <-- shared data-portal link, Canada Flood Susceptibility Index
    --
    doi.org/10.3390/ECWS-7-14235 <-- shared (2023) paper
    --
    doi.org/10.1002/2017WR020917 <-- shared (2017) paper
    --
    H/T @Michael DePue | VP & AtkinsRéalis Fellow for Water Resources Engineering | PE, PMP, CFM
    “At the Canadian Water Resources Association National Conference in Winnipeg, colleagues shared insights from Canada's Flood Hazard Identification and Mapping Program. This initiative has seen over 400 flood mapping projects and more than 1,000 flood hazard maps produced, supported by a substantial investment of $164.2 million from 2024 to 2028.
    Two key datasets:
    • The Canada Flood Map Inventory, which records the locations of flood hazard maps and provides information on how to access them.
    • The national Flood Susceptibility Index, a machine-learning assessment of flood-prone areas, including regions that have not been mapped in detail.
    When these two layers are combined on a single screen, it becomes clear where future mapping efforts should be directed — specifically, areas with high susceptibility that currently lack detailed maps…”

    @NRCAN

    #water #hydrography #flood #flooding #risk #hazard
  5. 💧🌏 Greg Cocks @[email protected] ·

    Advancing Detailed Flood Hazard Identification in Alberta, Canada - Insights from Two Recent Flood Studies
    --
    doi.org/10.3390/w18131592 <-- shared paper
    --
    “The increasing frequency of floods and the severity of their consequences for public safety, infrastructure, and the economy demand improved methods for flood hazard identification. Flood studies that include flood hazard mapping are critical tools for informing emergency response and flood recovery, as well as for land use and mitigation planning. The methodology for such flood studies has evolved, and access to more powerful computational resources and high-resolution base data has contributed to the increased use of two-dimensional hydraulic modelling, where one-dimensional modelling previously was the default. However, local-scale flood studies face real-world constraints, including sparse data, challenging hydrologic conditions, and budget limitations, which can hinder the application of advanced techniques. This study addresses these challenges through innovative, practice-driven solutions in two case studies in Alberta, Canada: a small, partly channelised prairie stream network (Wolf Creek, Lacombe) and a laterally dynamic river on a distributary delta (Swan River, Kinuso). Three core components of flood hazard studies are described: field survey data collection, regional hydrology assessment, and hydraulic modelling. Key findings include demonstrating that LiDAR-derived terrain models alone cannot capture channel conveyance, the importance of low-flow calibration in the absence of high-water marks, the selection of a modelling methodology based on bathymetric and topographic features within a study area, and the development of inflow hydrographs for unsteady-state simulation in flat floodplains…”
    #FloodMapping #FloodRisk #Hydrology #HydraulicModeling #HECRAS #WaterResources #Alberta #Resilience #RiverSurvey #spatialanlaysis #spatiotemporal #floodhazardmapping #HECRAS #model #modeling #remotesensing #LiDAR #bathymetry #floodfrequencyanalysis #unsteadysimulation #FHIMP #FHIP #WoldCreek #Lacombe #SwanRiver #Kinuso #Alberta #Canada #localscale #provincialfloodstudy # prairie #stream #river #flood #flooding #water #hydrology #risk #hazard #watershed #publicsafety #cost #damage #economics #infrastructure #use #practicedriven #floodhazard #survey #hydraulic #terrainmodels #hydrogeomorphology #topography #elevation #floodplain
    @Alberta Environment and Protected Areas | @Government of Alberta | @Barr Engineering

    #floodmapping #floodrisk #hydrology #hydraulicmodeling #hecras #waterresources
  6. 💧🌏 Greg Cocks @GregCocks ·

    Advancing Detailed Flood Hazard Identification in Alberta, Canada - Insights from Two Recent Flood Studies
    --
    doi.org/10.3390/w18131592 <-- shared paper
    --
    “The increasing frequency of floods and the severity of their consequences for public safety, infrastructure, and the economy demand improved methods for flood hazard identification. Flood studies that include flood hazard mapping are critical tools for informing emergency response and flood recovery, as well as for land use and mitigation planning. The methodology for such flood studies has evolved, and access to more powerful computational resources and high-resolution base data has contributed to the increased use of two-dimensional hydraulic modelling, where one-dimensional modelling previously was the default. However, local-scale flood studies face real-world constraints, including sparse data, challenging hydrologic conditions, and budget limitations, which can hinder the application of advanced techniques. This study addresses these challenges through innovative, practice-driven solutions in two case studies in Alberta, Canada: a small, partly channelised prairie stream network (Wolf Creek, Lacombe) and a laterally dynamic river on a distributary delta (Swan River, Kinuso). Three core components of flood hazard studies are described: field survey data collection, regional hydrology assessment, and hydraulic modelling. Key findings include demonstrating that LiDAR-derived terrain models alone cannot capture channel conveyance, the importance of low-flow calibration in the absence of high-water marks, the selection of a modelling methodology based on bathymetric and topographic features within a study area, and the development of inflow hydrographs for unsteady-state simulation in flat floodplains…”
    # prairie
    @Alberta Environment and Protected Areas | @Government of Alberta | @Barr Engineering

    #floodmapping #floodrisk #hydrology #hydraulicmodeling #hecras #waterresources
  7. 💧🌏 Greg Cocks @[email protected] ·

    Human Disturbances Dominated The Unprecedentedly High Frequency Of Yellow River Flood Over The Last Millennium
    --
    doi.org/10.1126/sciadv.adf8576 <-- shared paper
    --
    smithsonianmag.com/smart-news/ <-- shared technical article
    --
    #humanimpacts #anthropogenic #water #hydrography #hydrology #flood #flooding #river #catchment #engineering #riverine #YellowRiver #China #climatechange #extremeweather #floodhazard #risk #hazard #history #historic #sediment #reconstruction #spatialanalysis #spatiotemporal #levee #breaches #Holocene #geologic #fluvial #sustainability #planning #management #watermanagement #stress #change #dating #radiocarbon #OSL #sampling #paleoflood #overtopping #slackwater #sedimentation #model #modeling #embankment #riverstage #dams

    #humanimpacts #anthropogenic #water #hydrography #hydrology #flood
  8. 💧🌏 Greg Cocks @GregCocks ·

    Human Disturbances Dominated The Unprecedentedly High Frequency Of Yellow River Flood Over The Last Millennium
    --
    doi.org/10.1126/sciadv.adf8576 <-- shared paper
    --
    smithsonianmag.com/smart-news/ <-- shared technical article
    --

    #humanimpacts #anthropogenic #water #hydrography #hydrology #flood
  9. 💧🌏 Greg Cocks @[email protected] ·

    Canada’s National Elevation Data Strategy Update, November 2023
    --
    gogeomatics.ca/canadas-nationa <-- shared article
    --
    open.canada.ca/data/en/dataset <-- HRDEM home page
    --
    natural-resources.canada.ca/sc <-- FHIMP home page
    --
    #GIS #spatial #identification #mapping #Canada #airborne #topobathy #CCMEO #CHN #FHIMP #floodhazard #GeoAI #HRDEM #LiDAR #PointCloud #elevation #data #NationalHydroNetwork #NHN #flood #hazard #risk #floodrisk #3d #3ddata #funding #program #appliedscience #flooding #water #hydrology #hydrospatial #opendata #NRCan #province #terrority #model #modeling
    @NRCan

    #opendata #gis #spatial #identification #mapping #canada
  10. 💧🌏 Greg Cocks @GregCocks ·

    Canada’s National Elevation Data Strategy Update, November 2023
    --
    gogeomatics.ca/canadas-nationa <-- shared article
    --
    open.canada.ca/data/en/dataset <-- HRDEM home page
    --
    natural-resources.canada.ca/sc <-- FHIMP home page
    --

    @NRCan

    #gis #spatial #identification #mapping #canada #airborne
  11. 💧🌏 Greg Cocks @[email protected] ·

    National Feasibility Study For Flood Hazard Modeling And Mapping At A Regional Scale [RFP, Canada]
    --
    canadabuys.canada.ca/en/tender <-- request for proposal (RFP) link…
    --
    [wonderful to see such RFP’s coming out]
    #GIS #spatial #mapping #canada #rfp #requestforproposal #flood #flooding #hydrospatial #opportunity #model #modeling #usecase #appliedscience #hydrology #water #risk #hazard #floodhazard #FHIMP #casestudy #casestudies
    #NRCan #contract #contracting #contractingopportunities

    #gis #spatial #mapping #canada #rfp #requestforproposal
  12. 💧🌏 Greg Cocks @GregCocks ·

    National Feasibility Study For Flood Hazard Modeling And Mapping At A Regional Scale [RFP, Canada]
    --
    canadabuys.canada.ca/en/tender <-- request for proposal (RFP) link…
    --
    [wonderful to see such RFP’s coming out]

    #gis #spatial #mapping #canada #rfp #requestforproposal
  13. Max @mxf ·

    Assessing and Mitigating Ice-Jam Hazards and Risks: A European Perspective

    mdpi.com/2073-4441/15/1/76

    It would certainly be interesting to organize a similar workshop in , with a lot of work on-going in , both from a and perspective

    #flood #canada #icejam #modeling #floodhazard #floodrisk