首页|Nanchang University Researchers Update Current Data on Machine Learning (Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method)
Nanchang University Researchers Update Current Data on Machine Learning (Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method)
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
Investigators publish new report on artificial intelligence. According to news originating from Nanchang, People’s Republic of China, by NewsRx correspondents, research stated, “In the existing landslide susceptibility prediction (LSP) models, the influences of random errors in landslide conditioning factors on LSP are not considered, instead the original conditioning factors are directly taken as the model inputs, which brings uncertainties to LSP results. This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP uncertainties, and further explore a method which can effectively reduce the random errors in conditioning factors.” Financial supporters for this research include National Natural Science Foundation of China; China National Funds For Distinguished Young Scientists.
Nanchang UniversityNanchangPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
