کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7482944 | 1485261 | 2015 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Predicting fecal sources in waters with diverse pollution loads using general and molecular host-specific indicators and applying machine learning methods
ترجمه فارسی عنوان
پیش بینی منابع مدفوع در آبهای با بارهای آلودگی مختلف با استفاده از شاخص های عمومی و مولکولی میزبان و استفاده از روش های یادگیری ماشین
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کلمات کلیدی
آلودگی مدفوع، باکتری، باکتریوفاژ، بیفیدوباکتریوم، باکروئید، فراگیری ماشین، پیگیری منبع میکروبی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی های تجدید پذیر، توسعه پایدار و محیط زیست
چکیده انگلیسی
In this study we use a machine learning software (Ichnaea) to generate predictive models for water samples with different concentrations of fecal contamination (point source, moderate and low). We applied several MST methods (host-specific Bacteroides phages, mitochondrial DNA genetic markers, Bifidobacterium adolescentis and Bifidobacterium dentium markers, and bifidobacterial host-specific qPCR), and general indicators (Escherichia coli, enterococci and somatic coliphages) to evaluate the source of contamination in the samples. The results provided data to the Ichnaea software, that evaluated the performance of each method in the different scenarios and determined the source of the contamination. Almost all MST methods in this study determined correctly the origin of fecal contamination at point source and in moderate concentration samples. When the dilution of the fecal pollution increased (below 3 log10 CFU E. coli/100 ml) some of these indicators (bifidobacterial host-specific qPCR, some mitochondrial markers or B. dentium marker) were not suitable because their concentrations decreased below the detection limit. Using the data from source point samples, the software Ichnaea produced models for waters with low levels of fecal pollution. These models included some MST methods, on the basis of their best performance, that were used to determine the source of pollution in this area. Regardless the methods selected, that could vary depending on the scenario, inductive machine learning methods are a promising tool in MST studies and may represent a leap forward in solving MST cases.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Environmental Management - Volume 151, 15 March 2015, Pages 317-325
Journal: Journal of Environmental Management - Volume 151, 15 March 2015, Pages 317-325
نویسندگان
Arnau Casanovas-Massana, Marta Gómez-Doñate, David Sánchez, LluÃs A. Belanche-Muñoz, Maite Muniesa, Anicet R. Blanch,