Building Predictive Models for Noisy and Heterogeneous Data: An Application in Global Monitoring of Inland Water Dynamics

Freshwater, which is only available in inland water bodies such as lakes, reservoirs, and rivers, is increasingly becoming scarce across the world and this scarcity is posing a global threat to human sustainability. A global monitoring of inland water bodies is necessary for policy-makers and the scientific community to address this problem. The promise of data-driven approaches coupled with availability of remote sensing data presents opportunities as well as challenges for global monitoring. My research aims at developing predictive models that address the challenges in analyzing remote sensing data for creating the first global monitoring system of inland water dynamics.