{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T22:54:41Z","timestamp":1783983281545,"version":"3.55.0"},"reference-count":59,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T00:00:00Z","timestamp":1646870400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Grant 41901087"],"award-info":[{"award-number":["Grant 41901087"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Second Tibetan Plateau Scientific Expedition and Research program","award":["2019QZKK010206"],"award-info":[{"award-number":["2019QZKK010206"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Data-driven methods are commonly applied in avalanche hazard evaluation. However, few studies have tapped into the relationship between the explanatory variables and avalanche hazard in arid\u2013frigid areas, and the seasonal dynamics of avalanche hazard and its attribution has not been discussed. Therefore, to fill the gap in the hazard assessment of a dry\u2013cold snow avalanche, quantify the dynamic driving process of seasonal nonlinear explanatory variables on avalanche hazard, and improve the reliability of the assessments, this study used Support Vector Machine (SVM), Random Forest (RF) and K-Nearest Neighbour (KNN) algorithms to construct three assessment models; these were used and verified in the western Tianshan Mountains, China. The following results were obtained: The causative factors of avalanches varied based on the season. In winter, terrain and snow depth played a major role, whereas spring was mainly influenced by snow depth and meteorological factors. The dynamic process of avalanche hazard was mainly governed by the seasonality of snow depth and temperature. The seasonal changes in avalanche hazard increased from low to high. The performance of all models was consistent for season and more reliable than the inter-annual evaluations. Among them, the RF model had the best prediction accuracy, with AUC values of 0.88, 0.91 and 0.78 in winter, spring and the control group, respectively. The overall accuracy of the model with multi-source heterogeneous factors was 0.212\u20130.444 higher than that of exclusive terrain factors. In general, the optimised model could accurately describe the complex nonlinear collaborative relationship between avalanche hazard and its explanatory variables, coupled with a more accurate evaluation. Moreover, free from inter-annual scale, the seasonal avalanche hazard assessment tweaked the model to the best performance.<\/jats:p>","DOI":"10.3390\/rs14061340","type":"journal-article","created":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T20:19:10Z","timestamp":1646943550000},"page":"1340","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Winter\u2013Spring Prediction of Snow Avalanche Susceptibility Using Optimisation Multi-Source Heterogeneous Factors in the Western Tianshan Mountains, China"],"prefix":"10.3390","volume":"14","author":[{"given":"Jinming","family":"Yang","sequence":"first","affiliation":[{"name":"College of Resource and Environment Science, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Oasis Ecology, Ministry of Education, Xinjiang University, Urumqi 830046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"He","sequence":"additional","affiliation":[{"name":"China Institute of Desert Meteorology, CMA, Urumqi 830046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"CAS Research Center for Ecology and Environment of Central Asia, Urumqi 830011, China"},{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105559","DOI":"10.1016\/j.catena.2021.105559","article-title":"Mapping snow avalanche debris by object-based classification in moutainous regions from Sentinel-1 images and causative indices","volume":"206","author":"Liu","year":"2021","journal-title":"Catena"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1016\/j.scitotenv.2019.05.194","article-title":"Tree-ring reconstruction of snow avalanche activity: Does avalanche path selection matter?","volume":"684","author":"Favillier","year":"2019","journal-title":"Sci. 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