Estimating Snow-Related Daily Change Events in the Canadian Winter Season: A Deep Learning-Based Approach
Abstract
1. Introduction
2. Related Work
2.1. Progress in Snow Data Acquisition
2.2. Deep Learning Methods for Snow Modelling
3. Materials and Methods
3.1. Training Sample Processing and Labelling
3.2. Siamese Attention U-Net Architecture
3.3. The SSIM Index and the Contrastive Loss Function
3.4. Deriving the Frequency of Daily Snow Water Equivalent Change Events
3.5. SWE Change Point Detection
4. Results
4.1. Model’s Accuracy Metrics
4.2. Ablation Studies
4.3. Daily Snow Water Equivalent Change Events
4.4. Daily SWE Change Segments
4.5. Temperature Anomaly
4.6. Precipitation Anomaly
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Appendix B
Appendix C
References
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Threshold | TPR | FPR | TNR | FNR | PR | F1 | OA |
---|---|---|---|---|---|---|---|
40% | 100.00 | 100.00 | 0.00 | 0.00 | 30.60 | 0.47 | 30.60 |
45% | 100.00 | 37.04 | 62.94 | 0.00 | 54.34 | 70.0 | 74.28 |
46% | 100.00 | 12.56 | 87.44 | 0.00 | 77.84 | 88.0 | 91.29 |
47% | 100.00 | 3.52 | 96.48 | 0.00 | 92.60 | 96.00 | 97.56 |
48% | 100.00 | 0.77 | 99.23 | 0.00 | 98.29 | 99.00 | 99.47 |
50% | 98.61 | 0.00 | 100.00 | 1.39 | 100.00 | 99.30 | 99.57 |
Location | TPR | FPR | TNR | FNR | PR | F1 | N |
---|---|---|---|---|---|---|---|
Site A | 96.61 | 0.00 | 100.00 | 1.39 | 100.00 | 99.30 | 1882 |
Site B | 100.00 | 0.52 | 99.48 | 0.00 | 95.56 | 97.73 | 5500 |
Site C | 97.81 | 0.60 | 99.40 | 2.29 | 97.10 | 97.45 | 5169 |
Site D | 100.00 | 0.00 | 100.00 | 0.00 | 100.00 | 100.00 | 7410 |
Model Architecture | Loss Functions | Similarity Metrics | Accuracy Metrics | ||||||
---|---|---|---|---|---|---|---|---|---|
BCE | CL | ECD | SSIM | TPR | TNR | PR | F1 | Threshold | |
CNN base | Yes | No | Yes | No | 92.84 | 99.44 | 94.86 | 93.84 | 70% |
U-Net base | Yes | No | Yes | No | 89.56 | 97.12 | 77.55 | 83.12 | 65% |
U-Net base | Yes | No | No | Yes | 100.00 | 98.37 | 87.18 | 93.15 | 82% |
U-Net base | No | Yes | No | Yes | 96.71 | 99.96 | 99.60 | 98.14 | 75% |
Si-Att-UNet | Yes | No | Yes | No | 83.17 | 98.50 | 86.00 | 84.56 | 85% |
Si-Att-UNet | Yes | No | No | Yes | 100.00 | 99.14 | 92.82 | 96.28 | 70% |
Si-Att-UNet | No | Yes | No | Yes | 100.00 | 99.48 | 95.56 | 97.73 | 50% |
Month | S | tau | p-Value | R2 |
---|---|---|---|---|
January | 7291 | 0.16 | 1.4 × 10−1 | 5.0 × 10−2 |
February | 7128 | 0.13 | 2.7 × 10−1 | 3.0 × 10−2 |
March | 7275 | 0.25 | 2.6 × 10−2 | 1.3 × 10−1 |
April | 7310 | 0.38 | 7.9 × 10−4 | 2.7 × 10−1 |
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Malik, K.; Isteyak, I.; Robertson, C. Estimating Snow-Related Daily Change Events in the Canadian Winter Season: A Deep Learning-Based Approach. J. Imaging 2025, 11, 239. https://doi.org/10.3390/jimaging11070239
Malik K, Isteyak I, Robertson C. Estimating Snow-Related Daily Change Events in the Canadian Winter Season: A Deep Learning-Based Approach. Journal of Imaging. 2025; 11(7):239. https://doi.org/10.3390/jimaging11070239
Chicago/Turabian StyleMalik, Karim, Isteyak Isteyak, and Colin Robertson. 2025. "Estimating Snow-Related Daily Change Events in the Canadian Winter Season: A Deep Learning-Based Approach" Journal of Imaging 11, no. 7: 239. https://doi.org/10.3390/jimaging11070239
APA StyleMalik, K., Isteyak, I., & Robertson, C. (2025). Estimating Snow-Related Daily Change Events in the Canadian Winter Season: A Deep Learning-Based Approach. Journal of Imaging, 11(7), 239. https://doi.org/10.3390/jimaging11070239