首页|Studies in the Area of Machine Learning Reported from China University of Geosci ences (Landslide susceptibility assessment along the Karakoram highway, Gilgit B altistan, Pakistan: A comparative study between ensemble and neighbor-based mach ine ...)
Studies in the Area of Machine Learning Reported from China University of Geosci ences (Landslide susceptibility assessment along the Karakoram highway, Gilgit B altistan, Pakistan: A comparative study between ensemble and neighbor-based mach ine ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Fresh data on artificial intelligence are present ed in a new report. According to news reporting out of Wuhan, People’s Republic of China, by NewsRx editors, research stated, “This study addressed the complex challenges associated with landslide detection along the Karakoram Highway (KKH) , where tectonic events and data availability limitations posed significant obst acles.” Funders for this research include King Saud University. The news reporters obtained a quote from the research from China University of G eosciences: “To overcome these hurdles, the research framework encompassed sever al critical components. First, it tackled the issue of multicollinearity through the application of statistical measures such as Variable Inflation Factor (VIF) and Information Gain (IG). Secondly, the study emphasized the importance of sel ecting a study area that would comprehensively represent the multivariate landsc ape, with KKH serving as an illustrative example. In striving for an equilibrium between implementation ease and algorithmic performance, the research favored t he adoption of Random Forest (RF) and Extremely Randomized Trees (EXT) over XGBo ost. Lastly, to fine-tune the algorithms and optimize their parameters, the stud y employed Particle Swarm Optimization (PSO) and evaluated their performance usi ng metrics like the Area Under the Curve (AUC). Remarkably, this comprehensive a pproach yielded accuracy rates exceeding 90% for all algorithms te sted (RF, EXT, and K-Nearest Neighbor (KNN)), with specific AUC values of 0.967, 0.968, and 0.914, respectively.”
China University of GeosciencesWuhanPeople’s Republic of ChinaAsiaAlgorithmsCyborgsEmerging TechnologiesMa chine Learning
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