A spatial machine learning model developed from noisy data requires multiscale performance evaluation: Predicting depth to bedrock in the Delaware River Basin, USA
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Abstract
Spatial machine learning models can be developed from observations with substantial unexplainable variability, sometimes called ‘noise’. Traditional point-scale metrics (e.g., R2) alone can be misleading when evaluating these models. We present a multi-scale performance evaluation (MPE) using two additional scales (distributional and geostatistical). We apply the MPE framework to predictions of depth to bedrock (DTB) in the Delaware River Basin. Geostatistical analysis shows that approximately one third of the DTB variance is at spatial scale smaller than 2 km. Hence, we interpret our point-scale R2 of 0.3 (testing data) to be sufficient for regional-scale modelling. Bias-correction methods improve performance at two of the three MPE scales: point-scale change is negligible, while distributional and geostatistical performance improves. In contrast, bias correction applied to a global DTB model does not improve MPE performance. This work encourages scale-appropriate performance evaluations to enable effective model intercomparison.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | A spatial machine learning model developed from noisy data requires multiscale performance evaluation: Predicting depth to bedrock in the Delaware River Basin, USA |
| Series title | Environmental Modelling & Software |
| DOI | 10.1016/j.envsoft.2024.106124 |
| Volume | 179 |
| Year Published | 2024 |
| Language | English |
| Publisher | Elsevier |
| Contributing office(s) | Maryland Water Science Center, National Water Quality Program, WMA - Earth System Processes Division, Maryland-Delaware-District of Columbia Water Science Center |
| Description | 106124, 12 p. |
| Country | United States |
| State | Delaware, New Jersey, New York, Pennsylvania |
| Other Geospatial | Delaware River basin |
| Google Analytic Metrics | Metrics page |