Using Machine Learning to Understand the Hydrologic Impacts of Permafrost Thaw-Driven Land Cover Change
Bibliographic record
Abstract
Discontinuous permafrost regions are experiencing a change in land cover distribution as a result of permafrost thaw. In wetlands interspersed with discontinuous permafrost, climate change is particularly problematic because temperature increases can result in significant permafrost thaw, thaw-driven landscape changes, and resultant changes in watershed hydrologic responses. \n \nThe influence of land cover change on the short- and long-term hydrological responses of wetland-peatland complexes is poorly understood. A better understanding of the impacts of climate-related land cover evolution on the hydrology of wetland-covered watersheds requires information about the distribution of hydrologically important lands, their pattern, and the rate at which they change over time. Here, we first developed a machine learning-based land cover evolution model (TSLCM) to estimate the long-term evolution of dominant land covers for application to the discontinuous permafrost regions of Northern Canada. This model is applied to replicate historical land cover and estimate future land cover scenarios at the Scotty Creek Research Basin in the Northwest Territories, Canada. \n \n \n A significant challenge when analyzing land cover change effects on hydrological properties is generating time-dependent classified maps of the region of interest, and the challenges associated with preprocessing remotely sensed data for discriminating between wetlands and forest-covered regions. In this work, we focus on two important objectives supporting the improved classification of wetlands in discontinuous permafrost regions: the exclusive use of only RGB imagery, and the use of an image segmentation method to accelerate the automatic classification of land cover. A semantic segmentation neural network, a multi-layer perceptron (MLP), and watershed function algorithms are applied to develop the taiga wetland identification neural network (TWINN) for the hydrological classification of wetlands. TWINN is here demonstrated to accurately classify high-resolution imagery of discontinuous permafrost regions within the Northwest Territories into the water, forest, and wetlands, and also able to delineate the runoff area of wetlands. \n \nTo study the effect of land cover evolution on runoff generation in the Scotty Creek basin, the products of TWINN and TSLCM are used to inform a process-based hydrological model where land cover change is represented explicitly. According to simulation results, land cover transitions can modify annual mean streamflow by as much as 7%, in addition to influences due to changing precipitation regimes alone.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".