Exploring Machine Learning with Geospatial technology in Climate Change, Cryosphere and Agriculture
Exploring Machine Learning with Geospatial technology in Climate Change, Cryosphere and Agriculture
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I am currently engaged in a teaching and research position at Rashtriya Raksha University (INI, MHA), India, where I instruct courses on Remote Sensing, GIS, Disaster Management, and the application of Remote Sensing technologies in disaster management. In addition to my academic role, I have gained valuable industry experience as a GIS Engineer at RMSI Pvt. Ltd., where I contributed to various geospatial projects within a multinational corporate environment.
I hold a Master’s in Geoinformatics from the Central University of Jharkhand and a Bachelor's in Geography from Vidyasagar University, with a strong foundation in remote sensing, physical, and social geography. My research interests include glaciology, precision agriculture, and the impact of climate change on glacier dynamics and crop dynamics.
As an avid traveler, I relish exploring diverse landscapes and capturing their splendor through my lens, blending my love for adventure with my passion for photography.
My journey has led me to seek a prestigious Ph.D. position, where I aim to further contribute to the intricate understanding of Earth's systems. Explore my portfolio to glimpse the depth of my expertise and research endeavors.
Volunteering
Volunteering
Young Warrior - U-Report India
UNICEF
U-Report ID : 5048415
Anmay Foundation
Non-Profit Organization
Noida, Uttar Pradesh
Field of Interest
Field of Interest
Geospatial Modeling in Precision Agriculture.
EO based-ML Prediction Model for Hazard Vulnerability and Risk Assessment.
Climate Change Impact Analysis over Global and Regional Environments.
Remote Sensing and GIS Modeling Application in Cryosphere.
Participations / Capacity Building Program
Participations / Capacity Building Program
1. Conference Presentation on "Micro Climate Induced Hazard Dynamics Over Highly Dense Urban area of Ahmedabad, India"., The Natural Hazards Engineering Research Infrastructure (NHERI), Graduate Student Council (GSC).
(21st June 2024)
2. Faculty Development Program; on "Disaster Risk Management, Social Entrepreneurship, and Resilience. Organized by GNLU Collaboration with IUINDRR-NIDM.
(26th–28th, 2024)
3. Introduction to SAR Data Processing and Application organized by Department of Geo-Informatics Central University of Jharkhand, Ranchi.
(17th to 20th April,2023)
4. Application of Geoinformatics and Electrical Resistivity Techniques in Ground Water Investigations, Central University of Jharkhand, Ranchi.
(12th to 14th April,2023)
5. IEEE - GRSS International School of Soil Moisture School - 2023, organized by IEEE Bombay Chapter with IIT Bombay.
(15th to 17th March,2023)
6. Summer Training Program on Spatial Data Programming with R, GEE, and Python for Natural Resource Application organized by Indian Society of Geomatics, Ranchi Chapter.
(2nd to 8th June,2022)
7. Workshop on Remote Sensing of Cryosphere (WRSC 2023). Organizer, WRSC 2023 & Chair, IEEE GRSS Bombay Chapter.
(23rd to 25th May, 2023)
About Master's Dissertation
About Master's Dissertation
Title: “Satellite-based Crop Yield Estimation Using Machine Learning Algorithm of Jharkhand, India”
Supervisor: Dr. Bikash Ranjan Parida
(Associate Professor, CUJ, India)
The study includes various datasets collected from different satellite-based input sets and agriculture statistic data from government sources available for public use. The study employed a robust methodology to predict crop yield at a pixel level using machine learning algorithms. The study focused on Jharkhand (India), Utilizing satellite and climate data. Satellite data, specifically MODIS surface reflectance data, was leveraged to calculate vegetation indices (MCARI-II, WDRVI), enhancing the understanding of crop health and growth stages. Climate data, including parameters like Precipitation data, Temperature data, Vapor Pressure Deficit data, Solar Radiation data, and Evapotranspiration data. Based on the sample dataset study has predicted the yield using Random Forest ML algorithm.
Special Lectures
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