Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States
- Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)
- Univ. of Vermont, Burlington, VT (United States)
- City Univ. of New York (CUNY), NY (United States)
- Univ. of California, Davis, CA (United States)
- University of Minnesota, Saint Paul, MN (United States)
- McGill Univ., Montreal, QC (Canada)
- Dundalk Institute of Technology (Ireland)
- Northern Region Water Board, Mzuzu (Malawi)
- Rensselaer Polytechnic Inst., Troy, NY (United States)
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Univ. of Wisconsin, Madison, WI (United States)
- Cary Inst. of Ecosystem Studies, Millbrook, NY (United States)
Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the “black-box” nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having “no change” (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA); National Science Foundation (NSF); Natural Sciences and Engineering Research Council of Canada (NSERC)
- DOE Contract Number:
- 89233218CNA000001
- OSTI ID:
- 2377932
- Report Number(s):
- LA-UR--23-21976
- Country of Publication:
- United States
- Language:
- English
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