首页|Ministry of Ecology and Environment Reports Findings in Machine Learning (Simplification and simulation of evaluation process for low efficiency constructed wet lands based on principal component analysis and machine learning)
Ministry of Ecology and Environment Reports Findings in Machine Learning (Simplification and simulation of evaluation process for low efficiency constructed wet lands based on principal component analysis and machine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is the subject of a report. According to news reporting from Nanjing, People’s Republic of China, by NewsRx journalists, research stated, “The existing performance e valuation process of constructed wetlands (CWs) is complex, with shortcomings in both simplification of method and construction of simulation model, especially for low-efficiency CWs (LECWs, with an average close-degree calculated by the en tropy weight method being <0.6). This study presents a case study of LECWs in the Ningxia region (comprising 13 subsurface flow constructed wetlands (SSF CWs) and 7 surface flow constructed wetlands (SF CWs)), employs the entropy weight method (EWM) to construct an evaluation of CW operational effi ciency, simplifies evaluation indicators through principal component analysis (P CA), develops two random forest (RF) models to validate the rationality of the s implified indicators, and establishes simulation models by logistic regression ( LR).”
NanjingPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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