Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news originating from Wuhan, People's Republ ic of China, by NewsRx correspondents, research stated, "The southeastern Tibeta n Plateau (SETP) is a construction area of several key infrastructure projects i n China, such as the Sichuan-Tibet Railway and hydropower developments, which ha s historically faced the threat of glacier-related debris flows. However, a robu st assessment of such debris flow susceptibility is a challenge due to the compl ex and variable climate, terrain and glacial environment." Our news journalists obtained a quote from the research from the Chinese Academy of Sciences, "In this study, we used the hybrid models that combine statistical techniques (certainty factors, CF) with machine learning methods (logistic regr ession, LR; random forest, RF; extreme gradient boosting, XGBoost) to more accur ately identify debris flow susceptible (DFS) areas. Topography, geology, and hyd rological factors including glaciers and snow cover were used in these models to assess the DFS. Results show that 21 % to 42 % of t he study area is very high susceptible to debris flows, particularly from Ranwu to Bomi and around Namcha Barwa. The hybrid models effectively enhance the accur acy of the DFS assessments. The CF-RF model showed the greatest improvement, wit h an 8.4 % increase in accuracy compared to the single model, the DFS spatial distribution of which aligns closely with field survey results. The glacial area ratio and annual snowmelt positively impact DFS accuracy, ranking 2 nd and 9th in the factor importance, respectively."
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