生态沟渠是中国南方地区农业面源重要拦截磷污染的措施,受生态沟渠自身特征和环境因子影响,不同研究中生态沟渠对磷的去除效率差异较大。通过文献检索平台CNKI和Web of Science,收集研究磷在生态沟渠迁移转化过程的文献,经过筛选后,建...生态沟渠是中国南方地区农业面源重要拦截磷污染的措施,受生态沟渠自身特征和环境因子影响,不同研究中生态沟渠对磷的去除效率差异较大。通过文献检索平台CNKI和Web of Science,收集研究磷在生态沟渠迁移转化过程的文献,经过筛选后,建立生态沟渠磷迁移数据库,共包含334项数据,通过统计分析系得到生态沟渠对总磷(Total Phosphorus,TP)的平均去除效率为47.16%。基于Mann-Whitney U检验和K-W检验方法,分析不同因素(植被类型、沟渠材质类型、强化措施、气候温度、水力停留时间)对生态沟渠TP去除效率的影响。研究结果表明:不同植被类型中,多种人工植被的生态沟渠去除效果最好,其TP平均去除效率为53.93%;不同沟渠材质中,边坡半衬砌的生态沟渠的TP平均去除效率为58.22%,效果最佳;不同强化措施类型中,基质类更有助于提高生态沟渠对TP的去除效果,其去除平均去除效率达到53.53%;不同气候温度区间中,温度在>25~35℃时生态沟渠对TP去除效率最高,平均值为57.18%;不同水力停留时间条件下,超过24 h时生态沟渠对TP的平均去除效率最佳,达到72.12%。该研究成果可为中国南方生态沟渠磷拦截效应评估、沟渠设计提供技术支撑。展开更多
Two statistical validation methods were used to evaluate the confidence level of the Total Column Ozone (TCO) measurements recorded by satellite systems measuring simultaneously, one using the normal distribution and ...Two statistical validation methods were used to evaluate the confidence level of the Total Column Ozone (TCO) measurements recorded by satellite systems measuring simultaneously, one using the normal distribution and another using the Mann-Whitney test. First, the reliability of the TCO measurements was studied hemispherically. While similar coincidences and levels of significance > 0.05 were found with the two statistical tests, an enormous variability in the levels of significance throughout the year was also exposed. Then, using the same statistical comparison methods, a latitudinal study was carried out in order to elucidate the geographical distribution that gave rise to this variability. Our study reveals that between the TOMS and OMI measurements in 2005 there was only a coincidence in 50% of the latitudes, which explained the variability. This implies that for 2005, the TOMS measurements are not completely reliable, except between the -50° and -15° latitude band in the southern hemisphere and between +15° and +50° latitude band in the northern hemisphere. In the case of OMI-OMPS, we observe that between 2011 and 2016 the measurements of both satellite systems are reasonably similar with a confidence level higher than 95%. However, in 2017 a band with a width of 20° latitude centered on the equator appeared, in which the significance levels were much less than 0.05, indicating that one of the measurement systems had begun to fail. In 2018, the fault was not only located in the equator, but was also replicated in various bands in the Southern Hemisphere. We interpret this as evidence of irreversible failure in one of the measurement systems.展开更多
This research focuses on the application of three soft computing techniques including Minimax Probability Machine Regression(MPMR),Particle Swarm Optimization based Artificial Neural Network(ANN-PSO)and Particle Swarm...This research focuses on the application of three soft computing techniques including Minimax Probability Machine Regression(MPMR),Particle Swarm Optimization based Artificial Neural Network(ANN-PSO)and Particle Swarm Optimization based Adaptive Network Fuzzy Inference System(ANFIS-PSO)to study the shallow foundation reliability based on settlement criteria.Soil is a heterogeneous medium and the involvement of its attributes for geotechnical behaviour in soil-foundation system makes the prediction of settlement of shallow a complex engineering problem.This study explores the feasibility of soft computing techniques against the deterministic approach.The settlement of shallow foundation depends on the parametersγ(unit weight),e0(void ratio)and CC(compression index).These soil parameters are taken as input variables while the settlement of shallow foundation as output.To assess the performance of models,different performance indices i.e.RMSE,VAF,R^2,Bias Factor,MAPE,LMI,U(95),RSR,NS,RPD,etc.were used.From the analysis of results,it was found that MPMR model outperformed PSO-ANFIS and PSO-ANN.Therefore,MPMR can be used as a reliable soft computing technique for non-linear problems for settlement of shallow foundations on soils.展开更多
文摘Two statistical validation methods were used to evaluate the confidence level of the Total Column Ozone (TCO) measurements recorded by satellite systems measuring simultaneously, one using the normal distribution and another using the Mann-Whitney test. First, the reliability of the TCO measurements was studied hemispherically. While similar coincidences and levels of significance > 0.05 were found with the two statistical tests, an enormous variability in the levels of significance throughout the year was also exposed. Then, using the same statistical comparison methods, a latitudinal study was carried out in order to elucidate the geographical distribution that gave rise to this variability. Our study reveals that between the TOMS and OMI measurements in 2005 there was only a coincidence in 50% of the latitudes, which explained the variability. This implies that for 2005, the TOMS measurements are not completely reliable, except between the -50° and -15° latitude band in the southern hemisphere and between +15° and +50° latitude band in the northern hemisphere. In the case of OMI-OMPS, we observe that between 2011 and 2016 the measurements of both satellite systems are reasonably similar with a confidence level higher than 95%. However, in 2017 a band with a width of 20° latitude centered on the equator appeared, in which the significance levels were much less than 0.05, indicating that one of the measurement systems had begun to fail. In 2018, the fault was not only located in the equator, but was also replicated in various bands in the Southern Hemisphere. We interpret this as evidence of irreversible failure in one of the measurement systems.
基金financially supported by High-end Foreign Expert program(G20190022002)Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJZDK201900102)Chongqing Construction Science and Technology Plan Project(2019-0045),that are gratefully acknowledged。
文摘This research focuses on the application of three soft computing techniques including Minimax Probability Machine Regression(MPMR),Particle Swarm Optimization based Artificial Neural Network(ANN-PSO)and Particle Swarm Optimization based Adaptive Network Fuzzy Inference System(ANFIS-PSO)to study the shallow foundation reliability based on settlement criteria.Soil is a heterogeneous medium and the involvement of its attributes for geotechnical behaviour in soil-foundation system makes the prediction of settlement of shallow a complex engineering problem.This study explores the feasibility of soft computing techniques against the deterministic approach.The settlement of shallow foundation depends on the parametersγ(unit weight),e0(void ratio)and CC(compression index).These soil parameters are taken as input variables while the settlement of shallow foundation as output.To assess the performance of models,different performance indices i.e.RMSE,VAF,R^2,Bias Factor,MAPE,LMI,U(95),RSR,NS,RPD,etc.were used.From the analysis of results,it was found that MPMR model outperformed PSO-ANFIS and PSO-ANN.Therefore,MPMR can be used as a reliable soft computing technique for non-linear problems for settlement of shallow foundations on soils.
