This study explores the loss or degradation of the ecosystem and its service function in the Liaohe estuary coastal zone due to the deterioration ofwater quality.Aprediction systembased on support vectormachine(SVM)-p...This study explores the loss or degradation of the ecosystem and its service function in the Liaohe estuary coastal zone due to the deterioration ofwater quality.Aprediction systembased on support vectormachine(SVM)-particle swarm optimization(PSO)(SVM-PSO)algorithm is proposed under the background of deep learning.SVM-PSO algorithm is employed to analyze the pollution status of the Liaohe estuary,so is the difference in water pollution of different sea consuming types.Based on the analysis results for causes of pollution,the control countermeasures of water pollution in Liaohe estuary are put forward.The results suggest that the water pollution index prediction model based on SVM-PSO algorithm shows the maximum error of 2.41%,the average error of 1.24%in predicting the samples,the root mean square error(RMSE)of 5.36×10^(−4),and the square of correlation coefficient of 0.91.Therefore,the prediction system in this study is feasible.At present,the water pollution status of Liaohe estuary is of moderate and severe levels of eutrophication,and the water pollution status basically remains at the level of mild pollution.The general trend is fromphosphorus moderate restricted eutrophication to phosphorus restricted potential eutrophication.To sumup,the SVM-PSO algorithm shows good sewage prediction ability,which can be applied and promoted in water pollution control and has reliable reference significance.展开更多
Suaeda salsa is an annual euhalophyte in estuarine wetlands.Soil properties of wetlands have an important influence on S.salsa growth.Therefore,the soil ecological thresholds is valuable for the restoration of degrade...Suaeda salsa is an annual euhalophyte in estuarine wetlands.Soil properties of wetlands have an important influence on S.salsa growth.Therefore,the soil ecological thresholds is valuable for the restoration of degraded S.salsa wetlands.The objectives of this present study were to analyze the soil physicochemical properties and evaluate the soil ecological thresholds in the typical degraded areas for S.salsa growth.Soil text components became coarser with increased sand contents and less clay contents,as the higher degree of wetland degradation.Meanwhile,the salt contents in different soil depths gradually increased with the increased degree of degradation of wetlands.Evident changes in soil water content,organic matter content,and cations concentrations were not observed,while the concentrations of these factors were higher in the soil layer of 0-10 cm than those in the 20-30 cm.The soil pH in the 0-10 cm soil layer was lower than that in the 20-30 cm.The content of the three available nutrients did not change evidently with the increasing degree of degradation.The optimum thresholds of soil salinity and water content were 7.073-16.613 g/kg and 31.8-63.2%,respectively.展开更多
基金National Key R&D Program of China(2019YFC1407700)National Natural Science Foundation of China(Grant 41606141)Study on the mechanisms of macrobenthos responses to oil spill based on MINE method.
文摘This study explores the loss or degradation of the ecosystem and its service function in the Liaohe estuary coastal zone due to the deterioration ofwater quality.Aprediction systembased on support vectormachine(SVM)-particle swarm optimization(PSO)(SVM-PSO)algorithm is proposed under the background of deep learning.SVM-PSO algorithm is employed to analyze the pollution status of the Liaohe estuary,so is the difference in water pollution of different sea consuming types.Based on the analysis results for causes of pollution,the control countermeasures of water pollution in Liaohe estuary are put forward.The results suggest that the water pollution index prediction model based on SVM-PSO algorithm shows the maximum error of 2.41%,the average error of 1.24%in predicting the samples,the root mean square error(RMSE)of 5.36×10^(−4),and the square of correlation coefficient of 0.91.Therefore,the prediction system in this study is feasible.At present,the water pollution status of Liaohe estuary is of moderate and severe levels of eutrophication,and the water pollution status basically remains at the level of mild pollution.The general trend is fromphosphorus moderate restricted eutrophication to phosphorus restricted potential eutrophication.To sumup,the SVM-PSO algorithm shows good sewage prediction ability,which can be applied and promoted in water pollution control and has reliable reference significance.
基金This work was funded by the National Key R&D Program of China(2019YFC1407700).
文摘Suaeda salsa is an annual euhalophyte in estuarine wetlands.Soil properties of wetlands have an important influence on S.salsa growth.Therefore,the soil ecological thresholds is valuable for the restoration of degraded S.salsa wetlands.The objectives of this present study were to analyze the soil physicochemical properties and evaluate the soil ecological thresholds in the typical degraded areas for S.salsa growth.Soil text components became coarser with increased sand contents and less clay contents,as the higher degree of wetland degradation.Meanwhile,the salt contents in different soil depths gradually increased with the increased degree of degradation of wetlands.Evident changes in soil water content,organic matter content,and cations concentrations were not observed,while the concentrations of these factors were higher in the soil layer of 0-10 cm than those in the 20-30 cm.The soil pH in the 0-10 cm soil layer was lower than that in the 20-30 cm.The content of the three available nutrients did not change evidently with the increasing degree of degradation.The optimum thresholds of soil salinity and water content were 7.073-16.613 g/kg and 31.8-63.2%,respectively.
