کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6962636 | 1452274 | 2016 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Predicting recreational water quality advisories: A comparison of statistical methods
ترجمه فارسی عنوان
پیش بینی توصیه های کیفیت آب تفریحی: مقایسه روش های آماری
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کلمات کلیدی
مدل رگرسیون، سنجش عملکرد، سلامتی ساحل، کیفیت آب،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزار
چکیده انگلیسی
Epidemiological studies indicate that fecal indicator bacteria (FIB) in beach water are associated with illnesses among people having contact with the water. In order to mitigate public health impacts, many beaches are posted with an advisory when the concentration of FIB exceeds a beach action value. The most commonly used method of measuring FIB concentration takes 18-24 h before returning a result. In order to avoid the 24Â h lag, it has become common to ”nowcast” the FIB concentration using statistical regressions on environmental surrogate variables. Most commonly, nowcast models are estimated using ordinary least squares regression, but other regression methods from the statistical and machine learning literature are sometimes used. This study compares 14 regression methods across 7 Wisconsin beaches to identify which consistently produces the most accurate predictions. A random forest model is identified as the most accurate, followed by multiple regression fit using the adaptive LASSO.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Environmental Modelling & Software - Volume 76, February 2016, Pages 81-94
Journal: Environmental Modelling & Software - Volume 76, February 2016, Pages 81-94
نویسندگان
Wesley Brooks, Steven Corsi, Michael Fienen, Rebecca Carvin,