dinung rai

Designing and executing a multi-objective forest restoration suitability analysis across all seven provinces of Nepal using the se.plan module within the SEPAL (FAO) platform. Preparing and integrating custom Google Earth Engine assets and developing training guidelines for MSc students.

Designed and delivered hands-on training on landslide susceptibility mapping using Machine Learning (Random Forest) with open-access geospatial data for MA Geography students. Developed end-to-end Python workflow covering spatial data preprocessing, SMOTE-based class imbalance handling, and spatially blocked cross-validation.

Identified and selected suitable sampling sites along 100 m contour intervals in Gatlang Forest. Conducted vegetation inventories, collected soil samples, and administered household surveys to support research objectives.

Collected and created shapefiles for study sites and identified villages within the 3 km boundary of the Sundarbans. Conducted site suitability analysis using Multi-Criteria Decision Analysis (MCDA) with Analytical Hierarchy Process (AHP) and calculated tourist carrying capacity.

Assisted the principal instructor in conducting hands-on training sessions in ArcGIS and Google Earth Engine. Provided individual support to trainees during practical exercises and maintained computers.

Grade: 3.25 out of 4.0

Pursued a Bachelor of Science in Forestry with a focus on forestry and wood technology, completing a thesis on landslide susceptibility mapping using remote sensing and machine learning.