Machine learning approaches to identify hydrochemical processes and predict drinking water quality for groundwater environment in a metropolis
Zhan Xie(Yibin University), Yunhui Zhang(Wuhan University of Technology), Xingjun Zhang(Yibin University), Si Chen(Beijing Normal University), Yang Chang(Ministry of Natural Resources), Rongwen Yao(Yibin University), Junyi Li(Ministry of Natural Resources), Weiting Liu(Southwest Jiaotong University), Yangshuang Wang(Southwest Jiaotong University)
Cited by 34
Related Papers
Single Cell Genome Amplification Accelerates Identification of the Apratoxin Biosynthetic Pathway from a Complex Microbial Assemblage
|PLoS ONE|2011|144
Bioelectrochemical enhancement of methane production in low temperature anaerobic digestion at 10 °C
|Water Research|2016|137
Application of bacterial cytological profiling to crude natural product extracts reveals the antibacterial arsenal of Bacillus subtilis
|The Journal of Antibiotics|2015|65
Effects of rhamnolipids on the cell surface characteristics of Sphingomonas sp. GY2B and the biodegradation of phenanthrene
|RSC Advances|2017|37
Characterization of a di-n-butyl phthalate-degrading bacterial consortium and its application in contaminated soil
|Environmental Science and Pollution Research|2018|35