Sandtank-ml: An educational tool at the interface of hydrology and machine learning

Lisa K. Gallagher, Jill M. Williams, Drew Lazzeri, Calla Chennault, Sebastien Jourdain, Patrick O’leary, Laura E. Condon, Reed M. Maxwell

Research output: Contribution to journalArticlepeer-review

Abstract

Hydrologists and water managers increasingly face challenges associated with extreme climatic events. At the same time, historic datasets for modeling contemporary and future hydrologic conditions are increasingly inadequate. Machine learning is one promising technological tool for navigating the challenges of understanding and managing contemporary hydrological systems. However, in addition to the technical challenges associated with effectively leveraging ML for understanding subsurface hydrological processes, practitioner skepticism and hesitancy surrounding ML presents a significant barrier to adoption of ML technologies among practitioners. In this paper, we discuss an educational application we have developed—Sandtank-ML—to be used as a training and educational tool aimed at building user confidence and supporting adoption of ML technologies among water managers. We argue that supporting the adoption of ML methods and technologies for subsurface hydrological investigations and management requires not only the development of robust technologic tools and approaches, but educational strategies and tools capable of building confidence among diverse users.

Original languageEnglish (US)
Article number3328
JournalWater (Switzerland)
Volume13
Issue number23
DOIs
StatePublished - Dec 1 2021

All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Biochemistry
  • Aquatic Science
  • Water Science and Technology

Keywords

  • Education
  • Hydrological modeling
  • Hydrology
  • Machine learning

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