Uncovering Hidden Water Insecurity: A Conflict-Augmented, Equity-Weighted Machine Learning Framework for Global SDG 6.1 Risk Assessment
Publisher: Sustainable Development
Author(s): Kiran Kishor Maharana, Prasenjit Bhavathankar, and Renuka Pawar
Date: 2026
Topics: Renewable Resources
Achieving Sustainable Development Goal 6.1—universal safe drinking water access by 2030—demands forward-looking risk intelligence that moves beyond trend extrapolation, yet monitoring frameworks report current coverage and cannot anticipate its deterioration. The authors merge WHO/UNICEF Joint Monitoring Programme, World Bank, ACLED conflict, ND-GAIN climate vulnerability and Fragile States Index data into a 232-country panel spanning 2000–2024, and predict from 15 features—eight socioeconomic and service-coverage, seven conflict-climate—whether safely managed coverage will fall below 50% 5 years ahead (𝑛 =776 country-years). Under country-grouped cross-validation a persistence baseline dominates rank-order discrimination (AUC = 0.968), a finding that should reframe how such models are judged: machine learning contributes through calibration, equity and attribution rather than ranking. Conflict and climate covariates improve calibration (Brier 0.190 vs. 0.196) and yield a Hidden Vulnerability Index, evaluated against seed, architecture and permutation controls, that ranks countries whose reported trajectories understate structural risk. Equity weighting narrows the equal-opportunity gap across income groups by 41.5%, and temporal attribution identifies the urban–rural gap as the fastest-rising predictor of future insecurity (𝛽 =+0.0157 pp/year). Only 40.5% of countries are projected to achieve SDG 6.1, with no low-income country on track.