首页|New Machine Learning Findings Reported from Guangdong University of Technology ( Machine Learning Insights Into the Evolution of Flood Resilience: a Synthesized Framework Study)
New Machine Learning Findings Reported from Guangdong University of Technology ( Machine Learning Insights Into the Evolution of Flood Resilience: a Synthesized Framework Study)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Investigators publish new report on Ma chine Learning. According to news reportingfrom Guangzhou, People's Republic of China, by NewsRx journalists, research stated, "Enhancing urbanresilience repr esented a viable strategy to mitigate flooding induced by intense human activiti es andclimate change. However, existing studies often concentrated on system at tributes or isolated resiliencecharacteristics, failing to offer a holistic eva luation of urban flood resilience performance."Funders for this research include National Natural Science Foundation of China ( NSFC), Program forGuangdong Intro-ducing Innovative and Enterpreneurial Teams.The news correspondents obtained a quote from the research from the Guangdong Un iversity of Technology,"Thus, it was imperative to develop a comprehensive floo d resilience framework that incorporatedthe resilience evolution process includ ing resistance, economic and function recovery. Consequently, thisstudy endeavo red to devise a synthesized framework for evaluating urban flood resilience, sub sequentlyemploying a Convolutional Neural Network (CNN) model for simulation. T he findings indicated that: (1)Guangzhou's maximum resistance capacity diminish ed from 0.52 to 0.50 as rainfall return periods altered,while Dongguan exhibite d the lowest resistance, decreasing from 0.42 to 0.40. Regarding functional recovery capacity, Guangzhou ranked highest (0.35) and Foshan lowest (0.19); (2) acc ording to TriangularFuzzy Number-based AHP (TFN-AHP) analysis, the area classif ied as highest in resilience decreased from15.6% to 12.1% of the total, whereas the low resilience area increased from 7.6% to 8.7%; (3) Zhuhaiand Zhaoqing were primarily clustered along the resistance axis, in contrast, Dongguan was distinguishedby its advancement alo ng the axis of functional recovery.(4) CNN simulations yielded precise outcomes,with the Area Under the Receiver Operating Characteristic Curve (AUC) and predi ctive accuracy (ACC)values exceeding 0.8,respectively."
GuangzhouPeople's Republic of ChinaA siaCyborgsEmerging TechnologiesMachine LearningGuangdong University of T echnology
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