Enhancing Coastal Flood Forecasting with Geospatial Analytics and Real-Time Data

Abstract Submission: Abstract:
Coastal flooding, exacerbated by climate change, poses a serious threat to natural ecosystems and human infrastructure, particularly in sensitive coastal forest regions. This study investigates the application of IBM’s Environmental Intelligence Suite (EIS) for short-term coastal flood prediction, focusing on the platform’s geospatial capabilities within Croatan National Forest. The EIS integrates high-resolution satellite imagery, real-time meteorological data, and advanced geospatial analytics to improve the accuracy of short-term flood forecasts.

Through geospatial modeling, coastal topography, tidal patterns, and precipitation data specific to the Croatan coastal forest region were incorporated. By employing machine learning algorithms, the research enhances prediction precision. The key innovation lies in EIS’s ability to integrate real-time climate and hydrological inputs, significantly advancing flood forecasting accuracy.

In the Croatan National Forest case study, EIS-driven flood models demonstrate a strong correlation between tidal changes, precipitation intensity, and flood severity. The results show that advanced geospatial analytics can simulate coastal flooding with unprecedented accuracy, enhancing early warning systems for coastal flood events. This approach offers valuable insights for protecting ecological habitats and socio-economic infrastructure from the growing impacts of climate change-induced flooding.

This study highlights the potential of innovative platforms like EIS for sustainable water resource management and coastal ecosystem protection. It provides a replicable framework for similar regions globally, offering strategic insights for proactive flood risk mitigation.

Keywords: IBM Environmental Intelligence Suite, coastal flood prediction, geospatial analytics, Croatan National Forest, machine learning, real-time data, disaster management, climate change.

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