HydroStorm is a web platform that leverages machine learning (ML) to revolutionize the planning and design of hydropower plants along rivers. It uses advanced satellite image processing to analyze map patterns and identify potential construction sites, offering actionable insights for civil engineers and architects. Article: https://medium.com/@hydrostorm1000/hydrostorm-d685c4fe4bd6
- Analyzes satellite images to identify optimal locations for hydropower plant construction.
- Maps dam statuses using colored dots, distinguishing between:
- Proposed
- Completed
- Under Construction
- Predicts plant types such as:
- Storage
- Pumped Storage
- Run-of-River
- Utilizes logistic regression trained on real-world datasets for accurate forecasting.
- Employs ML models like:
- Random Forest
- CT-GAN
- Multioutput Regressor
- Predicts parameters like:
- Dam height
- Reservoir capacity
- Reservoir area
- Uses eight distinct inputs, including:
- Water head height
- Wave energy
- Integrates datasets from India's hydropower projects.
- Collects data through remote sensors to ensure accuracy and scalability.
- Flask for server-side logic and API development.
- HTML, CSS, and JavaScript for a responsive and user-friendly interface.
- Logistic Regression, Random Forest, CT-GAN, and Multioutput Regressor for predictive modeling.
- Utilizes remote sensing technology for satellite image analysis and dataset generation.
HydroStorm aims to:
- Enhance project planning and sustainability in renewable energy development.
- Optimize resources for civil engineers and architects.
- Foster collaborative innovation in hydropower infrastructure design.
- Clone the repository:
git clone https://github.com/prithikaaa/AIHydroPwPlant.git cd HydroStorm