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Title: Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

Journal Article · · Environmental Science and Technology
ORCiD logo [1];  [2];  [3]; ORCiD logo [4];  [5];  [6];  [7];  [8];  [9]; ORCiD logo [10];  [9];  [11];  [12]
  1. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)
  2. Univ. of California, Davis, CA (United States)
  3. Dundalk Institute of Technology (Ireland)
  4. McGill Univ., Montreal, QC (Canada)
  5. Univ. of Vermont, Burlington, VT (United States)
  6. Rensselaer Polytechnic Inst., Troy, NY (United States)
  7. City Univ. of New York (CUNY), NY (United States)
  8. Northern Region Water Board, Mzuzu (Malawi)
  9. Univ. of Minnesota, Minneapolis, MN (United States)
  10. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
  11. Univ. of Wisconsin, Madison, WI (United States)
  12. Cary Institute of Ecosystem Studies, Millbrook, NY (United States)
+ Show Author Affiliations

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:
National Science Foundation (NSF); Natural Sciences and Engineering Research Council of Canada (NSERC); USDOE Laboratory Directed Research and Development (LDRD) Program
Grant/Contract Number:
89233218CNA000001
OSTI ID:
2426822
Report Number(s):
LA-UR--23-21976
Journal Information:
Environmental Science and Technology, Journal Name: Environmental Science and Technology Journal Issue: 11 Vol. 58; ISSN 0013-936X
Publisher:
American Chemical Society (ACS)Copyright Statement
Country of Publication:
United States
Language:
English

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54 ENVIRONMENTAL SCIENCES
K-means clustering
KGML
domain knowledge
limnology
machine learning
surface area
temporal change