首页|China University of Geosciences Reports Findings in Machine Learning (Revealing the drivers and genesis of NO3-N pollution classification in shallow groundwater of the Saying River Basin by explainable machine learning and pathway analysis ...)
China University of Geosciences Reports Findings in Machine Learning (Revealing the drivers and genesis of NO3-N pollution classification in shallow groundwater of the Saying River Basin by explainable machine learning and pathway analysis ...)
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New research on Machine Learning is the subject of a report. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “Nitrate (NO-N), as one of the ubiquitous contaminants in groundwater worldwide, has posed a serious threat to public health and the ecological environment. Despite extensive research on its genesis, little is known about the differences in the genesis of NO-N pollution across different concentrations.” Our news journalists obtained a quote from the research from the China University of Geosciences, “Herein, a study of NO-N pollution concentration classification was conducted using the Shaying River Basin as a typical area, followed by examining the genesis differences across different pollution classifications. Results demonstrated that three classifications (0-9.98 mg/L, 10.14-27.44 mg/L, and 28.34-136.30 mg/L) were effectively identified for NO-N pollution using Jenks natural breaks method. Random forest exhibited superior performance in describing NO-N pollution and was thereby affirmed as the optimal explanatory method. With this method coupling SEMs, the genesis of different NO-N pollution classifications was proven to be significantly different. Specifically, strongly reducing conditions represented by Mn, Eh, and NO-N played a dominant role in causing residual NO-N at low levels. Manure and sewage (represented by Cl) leaching into groundwater through precipitation is mainly responsible for NO-N in the 10-30 mg/L classification, with a cumulative contribution rate exceeding 80 %. NO-N concentrations ＞30 mg/L are primarily caused by the anthropogenic loads stemming from manure, sewage, and agricultural fertilization (represented by Cl and K) infiltrating under precipitation in vulnerable hydrogeological conditions. Pathway analysis based on standardized effect and significance further confirmed the rationality and reliability of the above results.”
BeijingPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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