Srinivas J’s Post

Sriya.AI's revolutionary LNM predicts US SE Coastal Inundation with nearly 100% accuracy. I am very happy to share the results of a very crucial "US SE Coastal Inundation" project our team did this week and shared the results with our project sponsor. We now have a meeting with NOAA next week and hopefully will help NOAA to predict Coastal Flooding better initially in the east coast and then even in the west coast. Our prediction accuracy is >99%. See results below. Target Variable Flood Risk ( Yes: MLLW >6.5 ft , No: MLLW<6.5ft) Flood Risk: 7.96 % For ALL FEATURES No. of Independent Features: 53 No. of Rows: 2073 Sriya ML: Accuracy: 93.01% Precision: 78.57% AUC: 0.931 SXI: Accuracy: 95.18% Precision: 90.90% AUC: 0.946 SXI++: Accuracy: 99.97% Precision: 99.95% AUC: 0.997 DATA SETS: 4 RIVERS SEA LEVELS LUNAR CYCLE WEATHER DATA 6 TO 7 DATA SETS TOGETHER TRUE LNM

Congrats to Sriya.AI team on their successful use of our Large Numerical Models #LNM prediction of coastal flooding with 99% precision and accuracy! Let’s chat about your use case to leverage #SXI+

Floods have complex causes, dangerous risks for communities, and let's face it- they're pretty messy to deal with. That includes on the planning and modeling side. In a recent paper, IRIS affiliates Félix Santiago-Collazo and Matt Bilskie with experts from Louisiana State University describe how they attempted to make compound flooding a little bit easier to understand. Instead of creating a large framework that accounts for any number of the small factors that influence flooding events, the authors put together an inundation model that prioritizes the most important factors in a flooding event. “The two most important mechanisms that drive inundation during an extreme meteorological event in a coastal watershed are precipitation (i.e., rainfall) and storm surge. Moreover, waves and astronomical tide mechanisms can enhance the total inundation and, even in some regions, can be the dominating factor.” Read more about how the team broke down these complicated events here: https://lnkd.in/eBBd-arC

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#ScienceHighlights This month, we feature Ghosh et al. 2023: A Novel Response Priority Framework for an Urban Coastal Catchment Using Global Weather Forecasts-Based Improved Flood Risk Estimates, published in the Journal of Geophysical Research: Atmospheres https://lnkd.in/eG-CduKW Highlight: A new response prioritization strategy has been proposed in this work based on improved near-real-time flood risk forecasts which utilize flood hazard forecasts along with socio-economic vulnerability values to facilitate flood management measures. Check out the Popular Summary on our website: https://lnkd.in/eaRwA_nu #floodrisk #floodforecasting #floodhazard #vulnerability #AGUPrecip #ECSPrecip

Prepare for the unexpected with Lepton Maps API. 🌍 Our API not only processes unstructured address inputs but also provides critical insights into natural disaster risks, including earthquake, cyclone, and flood risks. Get precise latitude, longitude, and confidence radius to stay ahead and protect your assets. Learn more: https://bit.ly/3AbNXdE #NaturalDisasters #RiskAssessment #LocationIntelligence #LeptonMaps #DisasterPreparedness #GeospatialData #APIs #SafetyFirst #GIS

I am pleased to announce the publication of my latest research on Glacial Lake Outburst Flood (GLOF) hazards in the Warwan and Bhut basins, which are part of the Chenab Basin in the Northwestern Himalayas, now available in Results in Earth Sciences. This study assesses the impacts of climate change on glacial lake stability, identifies lakes at high risk of outburst, and proposes mitigation measures to reduce downstream flood hazards. Using remote sensing data and hydrological modeling, we present a comprehensive risk assessment that highlights the urgency of integrating climate change projections, lake morphology, and flood propagation models to enhance early-warning systems and adaptive strategies in these vulnerable regions. https://lnkd.in/gVM3ynxE #GLOF #ClimateChange #Warwan #Bhut #Chenab

🌍 Harnessing GIS and Remote Sensing for Flood Surveillance and Prevention in Central Asia 🌍📡🛰️ Floods are a significant threat to Central Asia, causing extensive damage to lives and livelihoods. The Central Asian Flood Early Warning System (CAFEWS), supported by the World Bank and World Meteorological Organization, aims to mitigate these impacts by providing a unified online platform for predicting extreme weather and floods. 🌧️🌊 Applications of GIS and Remote Sensing in Flood Management: Real-Time Monitoring: 🛰️ Remote sensing technologies, such as satellite imagery, enable continuous monitoring of weather patterns and water levels. This real-time data is crucial for early detection of potential flood events. 📡🌦️ Flood Mapping: 🗺️ Geographic Information Systems (GIS) are used to create detailed flood maps, identifying vulnerable areas and helping in the planning of evacuation routes and emergency response strategies. 🚨🏞️ Risk Assessment: 📊 By integrating historical flood data with current observations, GIS can assess flood risks and predict future flood scenarios. This helps in making informed decisions about land use and infrastructure development. 🏗️🌐 Disaster Preparedness: 🛠️ Remote sensing data combined with GIS can simulate flood events, allowing authorities to prepare and implement effective disaster response plans. This proactive approach minimizes the catastrophic effects of floods. 🚑🌪️ Resource Allocation: 🚚 GIS helps in the efficient allocation of resources by identifying the most affected areas and prioritizing them for relief efforts. This ensures timely and targeted assistance to those in need. 🤝📍 By leveraging the power of GIS and remote sensing, we can enhance our ability to predict, prepare for, and respond to floods, ultimately safeguarding communities and reducing the devastating impacts of these natural disasters. 🌟🌍 #FloodManagement #GIS #RemoteSensing #DisasterPreparedness #ClimateAction #SustainableDevelopment #CentralAsia #CAFEWS #WorldBank #WMO #TechForGood #GeoSurveys #Hydrology #Hydrogeology #RiskManagement #EnvironmentalScience #Geospatial #WeatherMonitoring #EmergencyResponse Video by: The World Bank https://lnkd.in/dfpprigY

Cascading hazards (or ‘multi-hazards’) are events where one hazard triggers or increases vulnerability of a second hazard, such as a landslide that blocks a river and creates a flood. They pose a challenge to disaster managers since they can evolve quickly and the outcome can be difficult to predict. Field notes from Nepal on grounding perspectives by Meryl Kruskopf and Jacob Ramthun, SERVIR Global, keys# A Powerful Landscape; Cascading Hazards in Nepal; The Satellite Perspective; Looking Ahead https://lnkd.in/gdvEqbbp  

Happy to share a recent article led by my PhD scholar Indhu Dasari! In this study we have developed a framework for improving the hydrological model performance for flood prediction by considering the spatial variability of rainfall events.