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Update portfolio: new profile picture and simplified experience descriptions
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.github/workflows/pages.yml

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id: deployment
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uses: actions/deploy-pages@v4
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PROFILE_README.md

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# Badre Abderrahmane Alloul
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**Geospatial Data Scientist & Computational Hydrologist**
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*From research notebooks to production systems: flood, climate, energy.*
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**Geospatial Software Engineer & Computational Hydrologist**
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*Environmental Intelligence | Multi-Disciplinary Systems Design | Lyon, France*
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[![Portfolio](https://img.shields.io/badge/Portfolio-badibosspy.github.io-00d4aa?style=for-the-badge&logo=google-earth&logoColor=white)](https://badibosspy.github.io)
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[![Portfolio](https://img.shields.io/badge/Architecture_Portfolio-00d4aa?style=for-the-badge&logo=google-earth&logoColor=white)](https://badibosspy.github.io)
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[![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-0077b5?style=for-the-badge&logo=linkedin)](https://linkedin.com/in/badre-abderrahmane-alloul)
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[![Email](https://img.shields.io/badge/Email-badrallouldjazairi%40gmail.com-D14836?style=for-the-badge&logo=gmail&logoColor=white)](mailto:[email protected])
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[![Email](https://img.shields.io/badge/Scientific_Collaborate-D14836?style=for-the-badge&logo=gmail&logoColor=white)](mailto:[email protected])
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---
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## What I actually do
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I design and ship geospatial workflows that withstand production: flood-risk models, climate data pipelines, and energy-infrastructure analytics. Everything is tested, containerized, and documented.
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### 🌐 Unified Environmental Observation System (UEOS)
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I engineer architectures that bridge the gap between **Physical Simulation** and **Artificial Intelligence**. My workspace is a multi-layered topology where multi-spectral satellite signals converge with numerical PDE solvers to create actionable environmental foresight.
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## Core capability map
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| Domain | What I deliver |
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| :-- | :-- |
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| Computational Hydrology | Flood modeling, dam safety analytics, hydrological simulation (Wflow-SBM, HEC-HMS). |
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| Energy Systems | Hydropower optimization, renewable siting, off-grid least-cost electrification (OnSSET). |
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| Geospatial Engineering | High-performance raster/vector pipelines, spatial DB design, automated mapping. |
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```mermaid
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graph TD
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subgraph "I. DATA ASSIMILATION LAYER"
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A1[(Sentinel Multi-Spectral)]
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A2[(ERA5/CMIP6 Reanalysis)]
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A3[(In-Situ Sensor Networks)]
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end
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## Stack I actually use
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`Python · GeoPandas · Rasterio · GDAL · PostGIS · QGIS · Dask · xarray · NumPy · Pandas · SciPy · TorchGeo · U-Net · STAC · COG · AWS (S3/Lambda/EC2) · Docker · SLURM · FastAPI`
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subgraph "II. INTELLIGENCE & INFERENCE"
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B1{Latent Space Mapping}
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B2[Computer Vision: U-Net / SAM]
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B3[Physics-Informed ML]
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B4[Numerical Fluid Dynamics]
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end
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## Selected outcomes (concise)
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- **INRAe (2025)**: National flood-damage analysis cut from days to hours (Dask/xarray on SLURM); PyQGIS plugin (NDVI/EVI/NDWI/NBR) deployed to field teams with FR/EN docs.
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- **REOR20 (2023–2025)**: Serverless flood-risk data lake (S3/STAC/COG); country-scale DEM automation (GDAL/SAGA); FastAPI + PostGIS hazard API with sub-second reads.
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- **ENGIE / CNR (2021–2023)**: Hydropower forecasts (SCADA + ERA5), cascade dependency graph (NetworkX), PV/wind siting (GeoPandas + MCDA), OnSSET least-cost off-grid scenarios.
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- **ANBT (2019–2020)**: PostGIS schema for 150+ dams with sub-500 ms queries; IDF automation for ungauged basins; PyQGIS DEM/watershed tool for field teams.
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subgraph "III. SCALABLE ORCHESTRATION"
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C1[Dask / xarray / SLURM]
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C2[Cloud-Native Pipelines: COG/STAC]
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end
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## How I work
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- Reproducible: versioned environments, containers, tests.
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- Scalable: chunked arrays, lazy eval, HPC/SLURM patterns.
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- Observable: metrics, validation steps, and clear handover docs.
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- Direct: no buzzwords—only what runs and serves.
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subgraph "IV. KNOWLEDGE DELIVERY"
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D1[High-Frequency Hazard Mapping]
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D2[Resource Optimization: Water/Energy/Ag]
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end
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A1 & A2 & A3 --> B1
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B1 --> B2 & B3 & B4
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B2 & B3 & B4 --> C1
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C1 --> C2
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C2 --> D1 & D2
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style B1 fill:#0c0c0f,stroke:#00d4aa,stroke-width:2px;
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style C1 fill:#0c0c0f,stroke:#00d4aa,stroke-width:2px;
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```
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**Let’s build:** [badibosspy.github.io](https://badibosspy.github.io) · [LinkedIn](https://linkedin.com/in/badre-abderrahmane-alloul) · [Email](mailto:[email protected])
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### 🔬 Emerging Frontiers & Research Focus
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Currently accelerating at the intersection of **Physics** and **Deep Learning**:
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- **Geo-AI & Computer Vision**: Scaling **TorchGeo** and segmentation models (U-Net, DeepLabv3+) for automated landscape classification and change detection.
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- **Physics-Informed ML (PIML)**: Integrating hydrological constraints into stochastic models to achieve better extrapolation in climate-scarcity scenarios.
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- **Cloud-Native Scalability**: Architecting distributed workflows for multi-TB datasets (S3, BigQuery, Dask) to minimize latency in global-reach applications.
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---
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### 🔧 Technological Arsenal
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#### 🌍 Geospatial & Remote Sensing
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![GDAL](https://img.shields.io/badge/GDAL/OGR-00d4aa?style=flat-square&logo=gdal&logoColor=white)
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![Rasterio](https://img.shields.io/badge/Rasterio-00d4aa?style=flat-square)
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![GeoPandas](https://img.shields.io/badge/GeoPandas-00d4aa?style=flat-square)
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![QGIS](https://img.shields.io/badge/QGIS/PyQGIS-00d4aa?style=flat-square&logo=qgis&logoColor=white)
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![GEE](https://img.shields.io/badge/Google_Earth_Engine-00d4aa?style=flat-square&logo=google&logoColor=white)
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![STAC](https://img.shields.io/badge/STAC/COG-00d4aa?style=flat-square)
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#### 🌊 Water, Energy & Agriculture
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- **Simulation**: `Wflow-SBM` `TELEMAC-2D` `ANUGA` `HEC-HMS` `HEC-RAS`.
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- **Optimization**: `OnSSET` `Hydropower Cascade Modeling` `MCDA Site Suitability`.
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- **Analysis**: `Extreme Value Stats (GEV)` `IDF Automation` `Vegetation Indices (NDVI/EVI)`.
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#### 🤖 Advanced Computing & Data
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![Python](https://img.shields.io/badge/Python_Expert-3776AB?style=flat-square&logo=python&logoColor=white)
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![PyTorch](https://img.shields.io/badge/PyTorch/TorchGeo-EE4C2C?style=flat-square&logo=pytorch&logoColor=white)
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![xarray](https://img.shields.io/badge/xarray/Dask-00d4aa?style=flat-square)
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![Docker](https://img.shields.io/badge/Docker/Singularity-2496ED?style=flat-square&logo=docker&logoColor=white)
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![SLURM](https://img.shields.io/badge/SLURM_HPC-00d4aa?style=flat-square)
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#### 🗄️ Spatial Databases & Cloud
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![PostgreSQL](https://img.shields.io/badge/PostgreSQL-336791?style=flat-square&logo=postgresql&logoColor=white)
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![PostGIS](https://img.shields.io/badge/PostGIS-336791?style=flat-square&logo=postgis&logoColor=white)
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![BigQuery](https://img.shields.io/badge/BigQuery-4285F4?style=flat-square&logo=googlecloud&logoColor=white)
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![AWS](https://img.shields.io/badge/AWS_Ops-232F3E?style=flat-square&logo=amazonwebservices&logoColor=white)
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---
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### 🎨 Design Philosophy: The Digital Synthesis
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Complexity in the natural world requires simplicity in code. I design systems to be **Reproducible, Scalable, and Observable**. My motivation is to build the digital synthesis between the physical world and computational intelligence.
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[**Full Architecture Portfolio →**](https://badibosspy.github.io)[**Professional Connectivity →**](https://linkedin.com/in/badre-abderrahmane-alloul)
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> *"The future of physical engineering is a high-resolution simulation of reality."*

Pro_Profile_Picture.png

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index.html

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<div class="frame-ring"></div>
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</div>
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<img src="my_photo.jpg" alt="Badre Alloul" class="hero-photo">
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<img src="Pro_Profile_Picture.png" alt="Badre Alloul" class="hero-photo">
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</div>
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</div>
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<div class="exp-content">
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<ul class="exp-bullets">
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<li>Productionized a flood-damage prototype into a modular Python package with tests.</li>
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<li>Built and deployed a PyQGIS plugin (NDVI/EVI/NDWI/NBR) for field teams; delivered FR/EN docs and training.</li>
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<li>Dask/xarray workflows on SLURM for Sentinel-2 and ERA5-Land (TB-scale), cutting runtimes from days to hours.</li>
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<li>Integrated STAC (Copernicus, Theia) with hydrological stats (Q10/Q50/Q90, low-flow) for API export.</li>
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<li>Trained U-Net/DeepLabv3+ on multi-GPU SLURM for land-cover segmentation.</li>
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</ul>
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<p class="exp-summary">Productionized flood-damage research prototypes into tested Python packages while deploying Dask/xarray workflows on SLURM clusters for TB-scale satellite processing (Sentinel-2, ERA5-Land). Built PyQGIS plugins for vegetation indices and land-cover segmentation models (U-Net/DeepLabv3+) on multi-GPU infrastructure.</p>
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<div class="exp-content">
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<p class="exp-summary">Built production 2D flood pipelines and a serverless hazard platform.</p>
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<li>S3/STAC/COG data lake for hazard data; backbone for scalable ingestion and serving.</li>
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<li>Dask/xarray climate pipelines (NetCDF/GRIB) tuned for SLURM with preemptible nodes.</li>
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<li>FastAPI/Pydantic REST API on PostGIS hazard layers; sub-second responses for dashboards.</li>
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<li>Country-scale DEM automation (GDAL/SAGA) into analysis-ready COGs; removed manual steps.</li>
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<li>U-Net (TorchGeo, Sentinel-2) for building footprints in flood-damage exposure workflows.</li>
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</ul>
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<p class="exp-summary">Built production 2D flood-risk pipelines and a serverless hazard platform on AWS, architecting an S3/STAC/COG data lake for climate data. Deployed FastAPI REST APIs on PostGIS hazard layers and automated country-scale DEM processing (GDAL/SAGA) into analysis-ready COGs for exposure modeling.</p>
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<p class="exp-summary">Geospatial data systems for hydropower operations and renewable planning.</p>
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<li>Cloud reporting on S3 + Lambda for hydropower/climate indicators used in steering and regulatory reporting.</li>
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<li>SCADA + ERA5 fusion to forecast hydropower production; quantified curtailment and revenue scenarios for ENEDIS/RTE.</li>
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<li>Dam cascade graph (NetworkX) for dependency and risk analysis of interconnected assets.</li>
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<li>PV/wind site-suitability (GeoPandas + MCDA) to prioritize 5,000+ opportunities with grid-connection alignment.</li>
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<li>OnSSET least-cost electrification for off-grid sites (PV, wind, hybrid), combining ERA5, settlements, and reinforcement costs.</li>
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</ul>
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<p class="exp-summary">Developed geospatial data systems for hydropower operations and renewable energy planning. Built cloud reporting pipelines (S3/Lambda) for regulatory indicators, fused SCADA with ERA5 for production forecasting, and created PV/wind site-suitability models (GeoPandas + MCDA) prioritizing 5,000+ grid-aligned opportunities using OnSSET least-cost electrification.</p>
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<p class="exp-summary">Geospatial infrastructure for national water-resource planning and dam siting.</p>
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<li>Designed a PostgreSQL/PostGIS database for 150+ dams, achieving sub-500ms spatial query performance for planning applications.</li>
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<li>Optimized database performance via partitioned tables and multi-column GiST indexes, reducing complex spatial join latency from minutes to under 1 second.</li>
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<li>Automated derivation of IDF curves for ungauged basins, reducing manual analysis from hours to minutes and accelerating site-selection.</li>
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<li>Developed a PyQGIS plugin for automated DEM analysis and watershed delineation, enabling field teams to evaluate dam sites efficiently.</li>
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</ul>
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<p class="exp-summary">Built geospatial infrastructure for national water-resource planning and dam siting. Designed a PostgreSQL/PostGIS database for 150+ dams with sub-500ms spatial queries, automated IDF curve derivation for ungauged basins, and developed a PyQGIS plugin for DEM analysis and watershed delineation used by field teams.</p>
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