Sandtank-ML: An Educational Tool at the Interface of Hydrology and Machine Learning

• Published in: Water (Basel) Vol. 13; no. 23; p. 3328
• Main Authors: Gallagher, Lisa K., Williams, Jill M., Lazzeri, Drew, Chennault, Calla, Jourdain, Sebastien, O’Leary, Patrick, Condon, Laura E., Maxwell, Reed M.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2021; MDPI
• Subjects: Artificial intelligence; Climate change; Computer simulation; Datasets; Earth science; Education; ENVIRONMENTAL SCIENCES; hydrological modeling; Hydrology; Learning algorithms; Machine learning; STEM education; System effectiveness; Water management
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w13233328

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.