I'm a graduate student in geography at the University of Alabama, specializing in geospatial and water resources modeling with a concentration in surrogate flood inundation mapping. Currently, I work as a graduate researcher at the Surface Dynamics Modeling Lab, where I develop automated, Python-based geospatial frameworks that support operational research in surface water hydrology. With a strong foundation in GIS, remote sensing, and ESRI technologies, I focus on integrating spatial science, hydrology, and AI to build scalable tools for flood forecasting, inundation modeling, and decision-support systems. I'm passionate about solving real-world water challenges through automation, innovation, and interdisciplinary collaboration.
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All of my activities are available at my LinkedIN: Supath Dhital.
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You can reach me by using sdhital@crimson.ua.edu
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My works and experience are available at My Portfolio: https://supathdhital.com.np
- Enhancement of Low-Fidelity Flood Innundation Mapping (FIM) through Surrogate Modeling. (Access poster)
- Designed and deployed a WebGIS application with ArcGIS Online and Arcade for flood inundation extent dissemination.
- Implemented a database with PostgreSQL and a framework for dynamic evacuation route planning for flood risk
- Built a Python-based operational workflow for flood mapping through raster analysis with GDAL, rasterio, and the GEE API.
- Packaged a Python module using poetry for automatic processing of population and building exposure to flood maps.
- Created a Python package to extract and process global building footprints using geemap and GeoPandas.
- Short-term weather forecasting by adopting deep learning (LSTM): A study of Kaski district, Nepal.
- Exploring seasonal dynamics of Landsat 8 and 9 data on vegetation indices
- FIM evaluation framework
- Detailed comparison between terrain-based flood maps with HEC RAS outputs for the Neuse River, NC.