Eutrophication Assessment of Estuary Area in the Northern Part of Liaodong Bay in Summer Based on Artificial Neutral Network Method
CHEN Yun
ZHAO Qian
XU Guangpeng
Abstract:In order to control the nutrients pollution status and eutrophication level in the northern part of Liaodong Bay,the characteristics of temporal and spatial distributions of inorganic nitrogen,active phosphate,chemical oxygen demand and chlorophyll-a were analyzedbased on the field survey data in the summer of 2014-2016.The artificial neural network with the error back propagation algorithm was established for the eutrophication level assessment of this sea area.The results showed that the concentration of nutrients,chemical oxygen demand and chlorophyll-a in the northern part of Liaodong Bay were high in the year of 2014 and 2016 but low in 2015affected by the drought in the summer of 2015 and the large scale rainfall in the summer of 2016.Phosphate limitation was the main feature of the surveyed sea area in the year of 2014 and 2015,while in 2016 the northern part of the surveyed area was phosphate limitation and the southern part was nitrogen limitation.The assessment results using BP artificial neutral network showed that the eutrophication level in the surveyed area was high in the summer of 2014 and 2016,and low in the summer of 2015,which performed a "U" type features.The sea area with serious eutrophication mainly occurred in the Liaohe Estuary,the Daliaohe Estuary and the coastal area nearby.In the summer of 2014-2016,the eutrophication level in the Daliaohe Estuary maintained a high level,which was nearly the same as that in the Liaohe Estuary in 2014 and 2016,but much higher than that in the other sea area in the Northern part of Liaodong Bay in 2015.When using BP artificial neural network for eutrophication assessment,the contribution rate of each evaluation index can be comprehensively considered,the over reliance on nutrients can be avoid and the subjective errors can he reduced.Thus the BP artificial neural network could be a more objective and reasonable method for eutrophication evaluation.
Keywords:Liaodong Baynutrienteutrophicationartificial neural network
Publication Date:2017-01-01
Online Publishing Date:2026-05-22(First online date of this platform, not the publication date of the document)
Pages:10( 48-57 )
