Risk evaluation of thaw settlement using machine learning models for the Wudaoliang-Tuotuohe region, Qinghai-Tibet Plateau

• Machine learning models were applied in thaw settlement risk evaluation. • The underground ice content is the key factor leading to thaw settlement. • Random forest model performed better than traditional settlement index model. Climate warming has aggravated the occurrence of thaw settlement in permafrost region, but the associated risk has not been precisely assessed or understood. This study applied four machine learning models to explore and compare the spatial distribution of thaw settlement risk in the Wudaoliang-Tuotuohe region, Qinghai-Tibet Plateau, namely, naïve Bayesian, k-nearest neighbor, logistic model tree and random forest models. A total of 853 thaw settlement locations and 12 conditioning factors were used to train and validate the above four models. The results indicated that random forest model performed best with the highest accuracy. The risk map produced by random forest model implied that about 76.55% of thaw settlements were located in very high-risk regions, which only occupied 6.85% of study area. The volume ice content, active layer thickness and thawing degree days were the main factors leading thaw settlement. By further comparing the performances between random forest model and other three models, the overestimated and underestimated risk regions (Beiluhe and Tuotuohe basins), and imbalanced conditioning factors (altitude and slope angle) were determined. In contrast with similar studies, this research performed better in model construction and accuracy. The results can help designers to implement precautionary measures in thaw settlement risk management.