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Understanding Drought Awareness from Web Data -A Computer Vision Approach
  • +3
  • Mashrekur Rahman,
  • Samuel Sandoval Solis,
  • Thomas Harter,
  • Mahmoud Saeedimoghaddam,
  • Niv Efron,
  • Grey S Nearing
Mashrekur Rahman
Department of Land, Air & Water Resources, Davis, University of California

Corresponding Author:[email protected]

Author Profile
Samuel Sandoval Solis
Department of Land, Air & Water Resources, Davis, University of California
Thomas Harter
Department of Land, Air & Water Resources, Davis, University of California
Mahmoud Saeedimoghaddam
Department of Land, Air & Water Resources, Davis, University of California
Niv Efron
Google Research
Grey S Nearing
Google Research

Abstract

We used computer vision (U-Net) model to leverage Standardized Precipitation Evapotranspiration Index (SPEI), Google Trends Search Interest, and Twitter data to understand patterns with which people in Continental United States (CONUS) indicate awareness of and interest in droughts. We found significant statistical relationships between the occurrence of meteorological droughts, as measured by SPEI, and search interest on drought topics over CONUS. SI tends to lag meteorological drought by a period of 2-3 months, however relationships between meteorological drought and corresponding search interest varies significantly over CONUS in both space and time. People in states with increasingly drier conditions generally have become increasingly interested in drought topics. However, with worsening drought conditions in California, public search interest on drought topics in the state has not increased significantly between 2016 and 2020, despite the overall search interest being high. We additionally applied sentiment analysis on 5 million tweets related to droughts and found that public emotions towards drought have become more polarized over time.
19 Dec 2023Submitted to ESS Open Archive
21 Dec 2023Published in ESS Open Archive