首页|Researchers from Chinese Academy of Meteorological Sciences Report Findings in Support Vector Machines (Performance Benchmarking On Several Regression Models Applied In Urban Flash Flood Risk Assessment)
Researchers from Chinese Academy of Meteorological Sciences Report Findings in Support Vector Machines (Performance Benchmarking On Several Regression Models Applied In Urban Flash Flood Risk Assessment)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Current study results on Machine Learning - Support Vector Machines have beenpublished. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors,research stated, “To evaluate the performances of regression models applied in the urban flash flood riskassessment, the historical urban flash flood occurrences points were used to build the Voronoi polygonnetworks for calculating Ripley’s K values which can be adopted to be the risk value and the predictandsin regression. The first level risk indicators of hazard, vulnerability, sensitivity and exposure risk factors inthe risk assessment, as well as the sensitivity subordinate indicators of imperviousness and terrain factor,were listed to be the predictors in the regression model.”
BeijingPeople’s Republic of ChinaAsiaMachine LearningRisk and PreventionSupport Vector MachinesChinese Academy of Meteorological Sciences
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