📊🛰️ Data processing scripts, ML models, and Explainable AI results created as part of my Masters Thesis @ Johns Hopkins

Machine Learning based Drought Prediction

Spatiotemporal Analysis of Agricultural Drought Severity and Hotspots in Somaliland. It integrates MODIS-derived vegetation indices and CHIRPS precipitation data to identify and assess drought severity and hotspots over time.

Lesson materials for Module 2 (M2), "Open Climate Science for Agriculture"

Evaluation of extreme hydrometeorological phenomena such as droughts and low water periods, using the Standardized Precipitation Index (SPI), the Standardized Precipitation-Evapotranspiration Index (SPEI) and the Palmer Drought Severity Index (PDSI), in reference to water scarcity, water stress and water availability.

Application of the ARIMA model to forecast rainfall patterns. Leveraging time-series analysis techniques, it predicts future rainfall levels by analyzing historical data specifically from Bahawalnagar District, Punjab, Pakistan.

Application of the ARIMA model to forecast PET patterns. Leveraging time-series analysis techniques, it predicts future rainfall levels by analyzing historical data specifically from Bahawalnagar District, Punjab, Pakistan.

Application of the ETS model to forecast rainfall patterns. Leveraging time-series analysis techniques, it predicts future rainfall levels by analyzing historical data specifically from Bahwalnagar District, Punjab, Pakistan.

This project employs R programming to compute the Standardized Precipitation Evapotranspiration Index (SPEI), a widely used drought index. SPEI calculations were conducted for Bahawalnagar District, Punjab, Pakistan.

A study of the stress response of vegetation to drought situation through multispectral satellite imagery. Case of study of Como lake, summer 2022.