Predict Seasonal Maximum Freezing Depth Changes Using Machine Learning in China over the Last 50 Years

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2023-08, Vol.15 (15), p.3834
• Main Authors: Wang, Shuo, Sheng, Yu, Ran, Youhua, Wang, Bingquan, Cao, Wei, Peng, Erxing, Peng, Chenyang
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Classification; Climate change; Climatic data; Efficiency; Environmental aspects; Environmental science; Freezing; Freezing points; Frozen ground; Geospatial data; Learning algorithms; Machine learning; machine-learning mode; maximum freezing depth; Meteorological data; Methods; Permafrost; reanalysis data; Remote sensing; seasonally frozen ground; Simulation; Support vector machines; Terrestrial ecosystems; Weather stations
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15153834

Seasonal freezing depth change is important in many environmental science and engineering applications. However, such changes are rare at region scales, especially over China, in the long time series. In this study, we evaluated the annual changes in seasonal maximum freezing depth (MFD) over China from 1971 to 2020 using an ensemble-modeling method based on support vector machine regression (SVMR) with 600 repetitions. Remote sensing data and climate data were input variables used as predictors. The models were trained using a large amount of annual measurement data from 600 meteorological stations. The main reason for using SVMR here was because it has been shown to perform better than random forests (RF), k-nearest neighbors (KNN), and generalized linear regression (GLR) in these cases. The prediction results were generally consistent with the observed MFD values. Cross validation showed that the model performed well on training data and had a better spatial generalization ability. The results show that the freezing depth of seasonally frozen ground in China decreased year by year. The average MFD was reduced by 3.64 cm, 7.59 cm, 5.54 cm, and 5.58 cm, in the 1980s, 1990s, 2000s, and 2010s, respectively, compared with the decade before. In the last 50 years, the area occupied by the freezing depth ranges of 0–40 cm, 40–60 cm, 60–80 cm, 80–100 cm, and 120–140 cm increased by 99,300 square kilometers, 146,200 square kilometers, 130,300 square kilometers, 115,600 square kilometers, and 83,800 square kilometers, respectively. In addition to the slight decrease in freezing depth range of 100–120 cm, the reduced area was 29,500 square kilometers. Freezing depth ranges greater than 140 cm showed a decreasing trend. The freezing depth range of 140–160 cm, which was the lowest range, decreased by 89,700 square kilometers. The 160–180 cm range decreased by 120,500 square kilometers, and the 180–200 cm range decreased by 161,500 square kilometers. The freezing depth range greater than 200 cm, which was the highest reduction range, decreased by 174,000 square kilometers. Considering the lack of data on the change in MFD of seasonally frozen ground in China in recent decades, machine learning provides an effective method for studying meteorological data and reanalysis data in order to predict the changes in MFD.