Neural networks for dimensionality reduction of fluorescence spectra and prediction of drinking water disinfection by-products-Reference-Cited by-同舟云学术

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Neural networks for dimensionality reduction of fluorescence spectra and prediction of drinking water disinfection by-products

Published:2018-06 Issue: Volume:136 Page:84-94
ISSN:0043-1354
Container-title:Water Research
language:en
Short-container-title:Water Research

Author:

Peleato Nicolas M.,Legge Raymond L.,Andrews Robert C.

Funder

University of Toronto

Natural Sciences and Engineering Research Council of Canada

Canadian Water Network

Publisher

Elsevier BV

Subject

Pollution,Waste Management and Disposal,Water Science and Technology,Ecological Modeling,Environmental Engineering,Civil and Structural Engineering

Reference41 articles.

1. TensorFlow: large-scale machine learning on heterogeneous distributed systems;Abadi;None,2015

2. Standard methods for the examination of water and wastewater;APHA/AWWA/WEF;Stand. Methods,2012

3. Tracking natural organic matter (NOM) in a drinking water treatment plant using fluorescence excitation-emission matrices and PARAFAC;Baghoth;Water Res.,2011

4. Application of classification trees for predicting disinfection by-product formation targets from source water characteristics;Bergman;Environ. Eng. Sci.,2016

5. Classification and calibration of organic matter fluorescence data with multiway analysis methods and artificial neural networks: an operational tool for improved drinking water treatment;Bieroza;Environmetrics,2011

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