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

Anuj Karpatne, Vipin Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
EditorsXindong Wu, Alexander Tuzhilin, Hui Xiong, Jennifer G. Dy, Charu Aggarwal, Zhi-Hua Zhou, Peng Cui
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1530-1531
Number of pages2
ISBN (Electronic)9781467384926
DOIs
StatePublished - Jan 29 2016
Event15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 - Atlantic City, United States
Duration: Nov 14 2015Nov 17 2015

Publication series

NameProceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015

Other

Other15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
CountryUnited States
CityAtlantic City
Period11/14/1511/17/15

Keywords

  • Remote sensing
  • classification
  • ensemble learning
  • local learning

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