Developed an AI-driven flood prediction model using satellite imagery and hydrological data from sources like NASA, NOAA, and Sentinel-1. Integrated real-time rainfall and river discharge data to improve flood forecasts. Built a mobile/web platform where users receive early flood warnings and emergency response recommendations.
Used geospatial data and AI to identify optimal locations for solar and wind farms based on sunlight, wind speed, and terrain analysis.Developed a predictive energy demand model to match supply with community needs. Designed a microgrid management system to ensure efficient power distribution.
Developed an AI-driven deforestation detection system using high-resolution satellite imagery (Sentinel-2, PlanetScope). Used deep learning algorithms to detect forest loss and illegal logging activity. Integrated an alert system that notifies conservation agencies when deforestation hotspots are detected.
Built a mobile-first precision agriculture platform that combines drone-captured multispectral imagery, localized weather forecasts, and soil nutrient mapping to deliver actionable field-level recommendations to farmers in real time. Includes pest and disease early detection with image recognition and an offline-capable mobile app with SMS fallback for alerts.
Deployed a network of low-cost IoT air quality sensors across high-density urban zones, feeding data into a cloud-based analytics dashboard that generates real-time Air Quality Index (AQI) maps and automated health advisories pushed to residents via mobile notifications. Includes historical pollution trend analysis and an open API for researchers.
Developed a smart water management platform integrating IoT-enabled borehole sensors, satellite-derived precipitation forecasts, and machine learning models to predict water availability, detect contamination events, and optimize distribution schedules across community water networks. Features transparent water credit tracking to prevent over-extraction.
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