首页|Researchers from Louisiana State University Report New Studies and Findings in t he Area of Machine Learning (Estimation of Land Displacement In East Baton Rouge Parish, Louisiana, Using Insar: Comparisons With Gnss and Machine Learning Mode ls)
Researchers from Louisiana State University Report New Studies and Findings in t he Area of Machine Learning (Estimation of Land Displacement In East Baton Rouge Parish, Louisiana, Using Insar: Comparisons With Gnss and Machine Learning Mode ls)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Machine Learning is now available. According to news originating from Baton Rouge, Louisiana, by NewsRx correspondents, research stated, "Subsidence in southeastern Louisiana is a sig nificant geological issue caused by natural and human-induced factors like low-l ying topography and groundwater pumping. Human activities also led to coastal la nd loss and reduced sediment supply." Financial support for this research came from United States Geological Survey. Our news journalists obtained a quote from the research from Louisiana State Uni versity, "Satellitebased technologies such as Global Navigation Satellite Syste ms (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) are used to monit or subsidence. Louisiana has about 130 continuously operating reference stations (CORS) monitoring subsidence statewide. GNSS provides accurate point measuremen ts but limited spatial coverage. InSAR, however, detects ground deformation over large areas using satellitebased radar imagery. In response to this advantage, we employed Sentinel-1 SAR images from 2017 to 2021 to estimate the vertical di splacement in East Baton Rouge (EBR) Parish. Significant displacement is found i n urban and industrial areas, particularly in high- and medium-density residenti al areas. The significant subsidence area is between Denham Spring and Baton Rou ge faults, where residential areas experience displacement of -0.7 to -1 cm/year . The displacement variation in land use indicates significant annual subsidence in some buildings and infrastructure. Three strategic facilities in Baton Rouge Downtown experienced displacement, with -6.1 mm/yr in Downtown, -2.99 mm/yr at Horace Wilkinson Bridge, and -4.94 mm/yr at central railway station. In addition , machine learning is employed to estimate the vertical displacement in the stud y area. The K-Nearest Neighbors (KNN) model provides a comprehensive understandi ng of subsidence estimation among the GBR (Gradient Boosting Regression), RFR (R andom Forest Regression), and KNN models."
Baton RougeLouisianaUnited StatesN orth and Central AmericaCyborgsEmerging TechnologiesMachine LearningLoui siana State University
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