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Estimating monthly total nitrogen concentration in streams by using artificial neural network

He, Bin; Oki, Taikan; Sun, Fubao; Komori, Daisuke; Kanae, Shinjiro; Wang, Yi; Hyungjun, Kim; Yamazaki, Dai

Description

Artificial Neural Network (ANN) is a flexible and popular tool for predicting the non-linear behavior in the environmental system. Here, the feed-forward ANN model was used to investigate the relationship among the land use, fertilizer, and hydrometerological conditions in 59 river basins over Japan and then applied to estimate the monthly river total nitrogen concentration (TNC). It was shown by the sensitivity analysis, that precipitation, temperature, river discharge, forest area and urban...[Show more]

dc.contributor.authorHe, Bin
dc.contributor.authorOki, Taikan
dc.contributor.authorSun, Fubao
dc.contributor.authorKomori, Daisuke
dc.contributor.authorKanae, Shinjiro
dc.contributor.authorWang, Yi
dc.contributor.authorHyungjun, Kim
dc.contributor.authorYamazaki, Dai
dc.date.accessioned2015-12-07T22:31:28Z
dc.identifier.issn0301-4797
dc.identifier.urihttp://hdl.handle.net/1885/22796
dc.description.abstractArtificial Neural Network (ANN) is a flexible and popular tool for predicting the non-linear behavior in the environmental system. Here, the feed-forward ANN model was used to investigate the relationship among the land use, fertilizer, and hydrometerological conditions in 59 river basins over Japan and then applied to estimate the monthly river total nitrogen concentration (TNC). It was shown by the sensitivity analysis, that precipitation, temperature, river discharge, forest area and urban area have high relationships with TNC. The ANN structure having eight inputs and one hidden layer with seven nodes gives the best estimate of TNC. The 1:1 scatter plots of predicted versus measured TNC were closely aligned and provided coefficients of errors of 0.98 and 0.93 for ANNs calibration and validation, respectively. From the results obtained, the ANN model gave satisfactory predictions of stream TNC and appears to be a useful tool for prediction of TNC in Japanese streams. It indicates that the ANN model was able to provide accurate estimates of nitrogen concentration in streams. Its application to such environmental data will encourage further studies on prediction of stream TNC in ungauged rivers and provide a useful tool for water resource and environment managers to obtain a quick preliminary assessment of TNC variations.
dc.publisherAcademic Press
dc.sourceJournal of Environmental Management
dc.subjectKeywords: fertilizer; nitrogen; artificial neural network; fertilizer application; hydrometeorology; land use; nitrogen; river basin; river discharge; sensitivity analysis; streamwater; urban area; water resource; article; artificial neural network; calibration; co Artificial neural network; Land use; Nitrogen concentration; Stream water
dc.titleEstimating monthly total nitrogen concentration in streams by using artificial neural network
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume92
dc.date.issued2011
local.identifier.absfor050205 - Environmental Management
local.identifier.ariespublicationu4956746xPUB23
local.type.statusPublished Version
local.contributor.affiliationHe, Bin , Kyoto University
local.contributor.affiliationOki, Taikan , University of Tokyo
local.contributor.affiliationSun, Fubao, College of Medicine, Biology and Environment, ANU
local.contributor.affiliationKomori, Daisuke , University of Tokyo
local.contributor.affiliationKanae, Shinjiro, Tokyo Institute of Technology
local.contributor.affiliationWang , Yi, United Nations University
local.contributor.affiliationHyungjun, Kim, University of Tokyo
local.contributor.affiliationYamazaki, Dai, University of Tokyo
local.description.embargo2037-12-31
local.bibliographicCitation.startpage172
local.bibliographicCitation.lastpage177
local.identifier.doi10.1016/j.jenvman.2010.09.014
local.identifier.absseo960504 - Ecosystem Assessment and Management of Farmland, Arable Cropland and Permanent Cropland Environments
dc.date.updated2016-02-24T11:27:24Z
local.identifier.scopusID2-s2.0-77957769268
local.identifier.thomsonID000284441900020
CollectionsANU Research Publications

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