Paradise-theking/Eswatini-Water-Stress-ML

Machine learning-based one-month-ahead forecasting of hydroclimatic water stress in Eswatini using remote-sensing and climate data. # Machine Learning-Based Forecasting of Hydroclimatic Water Stress in Eswatini A leakage-aware machine learning framework and interactive forecasting application for **one-month-ahead prediction of hydroclimatic water stress in Eswatini** using remotely sensed and reanalysis-derived environmental variables. The project investigates whether hydroclimatic conditions observed at month **t** can provide useful predictive information about water stress at month **t+1**, with particular emphasis on chronological validation, leakage prevention, regularization, independent testing, and comparison against simple forecasting baselines. The trained forecasting pipeline is also integrated into a **FastAPI backend** and **TypeScript/Vite dashboard** for interactive model inference and historical Water Stress Index visualization. --- ## Research Overview Eswatini is vulnerable to recurrent drought, rainfall variability, increasing temperatures, and associated pressures on agriculture and water resources. Reliable short-term hydroclimatic forecasting could complement conventional drought monitoring by providing advance information to support climate-resilient water-resource management. This project develops a monthly water-stress forecasting framework using environmental variables including: - precipitation; - potential evapotranspiration (PET); - temperature; - soil moisture; - runoff; - surface runoff; - solar radiation; - dew point; - wind speed; and - lagged and accumulated hydroclimatic variables. The final modelling experiment uses **Ridge Regression** to forecast the Water Stress Index one month ahead. --- ## Forecasting Objective The forecasting problem is formulated as: > Use hydroclimatic information available at month **t** to predict the Water Stress Index at month **t+1**. This distinction is important because the project is designed as a genuine forecasting experiment rather than a same-month estimation exercise. The forecasting horizon is therefore: …