Estimating monthly total nitrogen concentration in streams by using artificial neural network
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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.author | He, Bin | |
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dc.contributor.author | Oki, Taikan | |
dc.contributor.author | Sun, Fubao | |
dc.contributor.author | Komori, Daisuke | |
dc.contributor.author | Kanae, Shinjiro | |
dc.contributor.author | Wang, Yi | |
dc.contributor.author | Hyungjun, Kim | |
dc.contributor.author | Yamazaki, Dai | |
dc.date.accessioned | 2015-12-07T22:31:28Z | |
dc.identifier.issn | 0301-4797 | |
dc.identifier.uri | http://hdl.handle.net/1885/22796 | |
dc.description.abstract | 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 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.publisher | Academic Press | |
dc.source | Journal of Environmental Management | |
dc.subject | Keywords: 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.title | Estimating monthly total nitrogen concentration in streams by using artificial neural network | |
dc.type | Journal article | |
local.description.notes | Imported from ARIES | |
local.identifier.citationvolume | 92 | |
dc.date.issued | 2011 | |
local.identifier.absfor | 050205 - Environmental Management | |
local.identifier.ariespublication | u4956746xPUB23 | |
local.type.status | Published Version | |
local.contributor.affiliation | He, Bin , Kyoto University | |
local.contributor.affiliation | Oki, Taikan , University of Tokyo | |
local.contributor.affiliation | Sun, Fubao, College of Medicine, Biology and Environment, ANU | |
local.contributor.affiliation | Komori, Daisuke , University of Tokyo | |
local.contributor.affiliation | Kanae, Shinjiro, Tokyo Institute of Technology | |
local.contributor.affiliation | Wang , Yi, United Nations University | |
local.contributor.affiliation | Hyungjun, Kim, University of Tokyo | |
local.contributor.affiliation | Yamazaki, Dai, University of Tokyo | |
local.description.embargo | 2037-12-31 | |
local.bibliographicCitation.startpage | 172 | |
local.bibliographicCitation.lastpage | 177 | |
local.identifier.doi | 10.1016/j.jenvman.2010.09.014 | |
local.identifier.absseo | 960504 - Ecosystem Assessment and Management of Farmland, Arable Cropland and Permanent Cropland Environments | |
dc.date.updated | 2016-02-24T11:27:24Z | |
local.identifier.scopusID | 2-s2.0-77957769268 | |
local.identifier.thomsonID | 000284441900020 | |
Collections | ANU Research Publications |
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