Drought Vulnerability Classifier for Smallholder Farmers: AI-Powered Decision Support System for Emergency Water Infrastructure Allocation

Machine Learning Classifier for Economic Vulnerability Assessment in Drought-Prone Agricultural Regions This CLASSIFIER leverages econometric modeling and machine learning to assess economic vulnerability of smallholder farmers in prolonged drought zones. Integrates 47 hydroclimatic, socioeconomic, and agronomic variables using ordered probit models to prioritize emergency distribution of rainwater harvesting systems and cisterns. Key Features: Multi-criteria vulnerability assessment; probabilistic classification with confidence intervals; geospatial risk mapping; automated beneficiary prioritization; scenario simulation for prospective planning; SHAP values for interpretability. Potential Impact: Can potentially reduce response time to water crises, improve allocation efficiency of public resources, and support evidence-based humanitarian water policies in arid and semi-arid regions globally. Technical Details: Python 3.11, statsmodels, scikit-learn, GeoPandas. TRL 7 (validated in relevant operational environment). Integrates real-time meteorological data via public APIs. Developed at Universidade Federal de Mato Grosso (UFMT) in partnership with research institutions and rural development agencies. Part of this content may have been AI-generated under supervision by Daniel Thomaz Giacomelli Nunes Maciel.