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

Machine Learning based Drought Prediction

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.

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.

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

Practical geospatial analyses using ChatGPT prompts and Google Earth Engine JS API—based on UNU course exercises for vegetation, air quality, drought, floods & urban planning.

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 platform designed to provide streamlined, user-friendly, and validated Combined Drought Indicator (CDI) products.

Computation of 14 Drought Indices (SPI, SPEI, PDSI, RAI, RD, ZSI, EDI, RDDI, PPN, HDSI, CZI, MCZI, PNI, and RDI) and Their Characteristics, Applicable to Daily and Monthly Data from One or More Stations, Using the R Programming Language