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

    Coupled Hydrological And Public Health Risks From Urban Flooding - Integrated Remote Sensing, Machine Learning, And Hydrodynamic–Ecological Modelling
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    doi.org/10.1016/j.jhydrol.2026 <-- shared paper
    --
    doi.org/10.1016/j.wroa.2025.10 <-- share (earlier) paper
    --
    H/T @RAHUL DEOPA | Research Scholar (IIT Roorkee)
    “… [U]rban floods are not merely hydraulic events; they also transport sewage, pathogens, and other contaminants across streets and communities, leading to significant public health risks…
    How do we quantify microbial contamination in near real time during a flood event, when emergency conditions make field sampling unsafe, sparse, or even impossible?...
    [The authors] explored whether Earth observation data, combined with machine learning, could bridge this critical monitoring gap. By combining Landsat-derived water surface temperature, machine learning, a coupled MIKE+ Flood–ECO Lab hydrodynamic–ecological model, and Quantitative Microbial Risk Assessment (QMRA), [they] estimated microbial concentrations (𝘌. 𝘤𝘰𝘭𝘪), simulated their fate and transport during floods, and quantified the associated human health risks.
    The takeaway: predicting flood risk isn't just about where the water goes; it's about what it's carrying and who it puts in harm's way. Earth observation and machine learning can help close that gap when it matters most, during the emergency, not weeks after…”
    #publichealth #risk #hazard #watersecurity #Floodrisk #Humanhealthrisk #Urbanflooding #Hydrodynamicmodelling #waterquality #model #modeling #SupportVectorRegression #flood #flooding #urban #city #sewage #pathogens #contaminant #disease #streets #community #quantification #remotesensing #GIS #spatial #mapping #earthobservation #spatialanalysis #water #hydrology #climatechange #extremeweather #spatiotemporal #AI #machineleraning #fateandtransport #hydrodynamic #microbial #rainfall #drainage #streamflow #topography #hydrogeomorphology #Delhi #India #floodplain

    #publichealth #risk #hazard #watersecurity #floodrisk #humanhealthrisk
  2. 💧🌏 Greg Cocks @GregCocks ·

    Coupled Hydrological And Public Health Risks From Urban Flooding - Integrated Remote Sensing, Machine Learning, And Hydrodynamic–Ecological Modelling
    --
    doi.org/10.1016/j.jhydrol.2026 <-- shared paper
    --
    doi.org/10.1016/j.wroa.2025.10 <-- share (earlier) paper
    --
    H/T @RAHUL DEOPA | Research Scholar (IIT Roorkee)
    “… [U]rban floods are not merely hydraulic events; they also transport sewage, pathogens, and other contaminants across streets and communities, leading to significant public health risks…
    How do we quantify microbial contamination in near real time during a flood event, when emergency conditions make field sampling unsafe, sparse, or even impossible?...
    [The authors] explored whether Earth observation data, combined with machine learning, could bridge this critical monitoring gap. By combining Landsat-derived water surface temperature, machine learning, a coupled MIKE+ Flood–ECO Lab hydrodynamic–ecological model, and Quantitative Microbial Risk Assessment (QMRA), [they] estimated microbial concentrations (𝘌. 𝘤𝘰𝘭𝘪), simulated their fate and transport during floods, and quantified the associated human health risks.
    The takeaway: predicting flood risk isn't just about where the water goes; it's about what it's carrying and who it puts in harm's way. Earth observation and machine learning can help close that gap when it matters most, during the emergency, not weeks after…”

    #publichealth #risk #hazard #watersecurity #floodrisk #humanhealthrisk