Development of a Drought Early Warning System based on the Prediction of Agricultural Productivity: A Data Science Approach

Drought is among the most common but least understood phenomena that affect an increasing number of people in context climate change. To understand underlying drought dynamics affecting local agricultural production Botswana, a broad database comprising climatic and remote-sensing data together with socioeconomic indicators was set up. A science approach includes statistical machine learning methods chosen to retrieve information applicable early-warning system. The aim study examine how can contribute understanding risk through integration various sources. Different regression models (including linear OLS) were applied. Naïve Bayes classification Random Forest included, as change point analysis. impacts two variables particular, Standardized Precipitation Index (SPI) Southern Oscillation (SOI), on crop productivity could be observed, highlighting possible national regional thresholds. Further development early warning system, including validation, should accompanied by ground-truth work partners.