首页|Zhengzhou University Reports Findings in Machine Learning (Predicting and analyz ing the algal population dynamics of a grass-type lake with explainable machine learning)
Zhengzhou University Reports Findings in Machine Learning (Predicting and analyz ing the algal population dynamics of a grass-type lake with explainable 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 th e subject of a report. According to news reporting originating from Henan, Peopl e's Republic of China, by NewsRx correspondents, research stated, "Algal blooms, exacerbated by climate change and eutrophication, have emerged as a global conc ern. In this study, we introduce a novel interpretable machine learning (ML) wor kflow tailored for investigating the dynamics of algal populations in grass-type lakes, Liangzi lake." Our news editors obtained a quote from the research from Zhengzhou University, " Utilizing seven ML methods and incorporating the covariance matrix adaptation ev olution strategy (CMA-ES), we predict algal density across three distinct time p eriods, resulting in the construction of a total of 30 ML models. The CMA-ES-Cat Boost model consistently demonstrates superior predictive accuracy and generaliz ation capability across these periods. Through the collective validation of vari ous interpretable tools, we identify water temperature and permanganate index as the two most critical water quality parameters (WQIs) influencing algal density in Liangzi Lake. Additionally, we quantify the independent and interactive effe cts of WQIs on algal density, pinpointing key thresholds and trends. Furthermore, we determine the minimum combination of WQIs that achieves near-optimal predic tive performance, striking a balance between accuracy and cost-effectiveness."
HenanPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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