首页|Findings in the Area of Machine Learning Reported from Chinese Academy of Scienc es (Thermokarst Landslides Susceptibility Evaluation Across the Permafrost Regio n of the Central Qinghai-tibet Plateau: Integrating a Machine Learning Model Wit h ...)
Findings in the Area of Machine Learning Reported from Chinese Academy of Scienc es (Thermokarst Landslides Susceptibility Evaluation Across the Permafrost Regio n of the Central Qinghai-tibet Plateau: Integrating a Machine Learning Model Wit h ...)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news reporting originating from Lanzhou, People's R epublic of China, by NewsRx correspondents, research stated, "Thermokarst landsl ides (TLs), which are made up of retrogressive thaw slumps (RTSs) and active-lay er detachments slides (ALDSs), are quickly increasing in the central Qinghai-Tib et Plateau (QTP) permafrost area. TLs induce many environmental problems and thr eaten the safety of infrastructure." Funders for this research include National Natural Science Foundation of China ( NSFC), Major Science and technology project of Gansu Province, Excellent Doctora l Pro-gramme of Gansu Province. Our news editors obtained a quote from the research from the Chinese Academy of Sciences, "A landslide susceptibility map is crucial to prevent the negative imp acts of these landslides. However, traditional thermokarst landslides susceptibi lity (TLS) evaluations do not consider real-time surface deformation information . Therefore, we propose a novel method that integrates a conventional machine le arning model with ground surface deformation. Nine influencing factors, includin g slope, normalized difference vegetation index, elevation, precipitation, thawi ng degree days, soil content, active layer thickness, water content, and vegetat ion type were selected based on the qindex detector, and support vector machine was employed to obtain an initial model (IM). We obtained surface deformation in the study area using the enhanced Small Baseline Subset (SBAS) method. The accu racy of the InSAR results was validated through comparison with data from two fi eld monitoring cross-sections. Subsequently, we established an integrated model (ITM) by combining the initial model with surface deformation using the contribu tion matrix, and confirmed the fusion model's higher rationality and accuracy th rough comparison with the IM. Furthermore, we examined the impact of the quadtre e segmentation method on atmospheric correction and validated the TLS results ob tained using the ITM with high-resolution optical remote sensing imagery from GF 6."
LanzhouPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningTechnologyChinese Academy of Sciences
NETL
NSTL
