Linked Life Data

18231871

Source:http://linkedlifedata.com/resource/pubmed/id/18231871

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1-4
pubmed:dateCreated
2008-12-10
pubmed:abstractText
This study aims to apply Moderate Resolution Imaging Spectroradiometer (MODIS Data) to monitor water quality parameters including chlorophyll-a, secchi disk depth, total phosphorus and total nitrogen at Chaohu Lake. In this paper, multivariate regression analysis, Back Propagation neural networks (BPs), Radial Basis Function neural networks (RBFs) and Genetic Algorithms-Back Propagation (GA-BP) were applied to investigate the relationships between water quality parameters and the MODIS bands combinations. The study results indicated that a simple, efficient and acceptable model could be established through multivariate regression analysis, but the model precision was relatively low. In comparison, BPs, RBFs and GA-BP were significantly advantageous in terms of sufficient utilization of spectra information and model reliance. The relative errors of BPs, RBFs and GA-BP were below 35%. Based on method comparison, it can be concluded that GA-BP is more suitable for simulation and prediction of water quality parameters by applying genetic algorithm to optimize the weight value of BP network. This study demonstrates that MODIS data can be applied for monitoring some of the water quality parameters of large inland lakes.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1573-2959
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
148
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
255-64
pubmed:dateRevised
2009-5-11
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Application of MODIS satellite data in monitoring water quality parameters of Chaohu Lake in China.
pubmed:affiliation
MOE Laboratory for Surface Processes, College of Environmental Sciences, Peking University, Beijing 100871, People's Republic of China.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't