To meet the increasing ：need of fresh water and to improve the water quality of Taihu Lake, water transfer from the Yangtze River was initiated in 2002. This study was performed to investigate the sediment distributi...To meet the increasing ：need of fresh water and to improve the water quality of Taihu Lake, water transfer from the Yangtze River was initiated in 2002. This study was performed to investigate the sediment distribution along the river course following water transfer. A rainfall-runoff model was first built to calculate the runoff of the Taihu Basin in 2003. Then, the flow patterns of river networks were simulated using a one-dimensional river network hydrodynamic model. Based on the boundary conditions of the flow in tributaries of the Wangyu River and the water level in Taihu Lake, a one-dimensional hydrodynamic and sediment transport numerical model of the Wangyu River was built to analyze the influences of the inflow rate of the water transfer and the suspended sediment concentration （SSC） of inflow on the sediment transport. The results show that the water transfer inflow rate and SSC of inflow have significant effects on the sediment distribution. The higher the inflow rate or SSC of inflow is, the higher the SSC value is at certain cross-sections along the ：river course of water transfer. Higher inflow rate and SSC of inflow contribute to higher sediment deposition per kilometer and sediment thickness. It is also concluded that a sharp decrease of the inflow velocity at the entrance of the Wangyu River on the river course of water transfer induces intense sedimentation at the cross-section near the Changshu hydro-junction. With an increasing distance from the Changshu hydro-junction, the sediment deposition and sedimentation thickness decrease gradually along the river course.展开更多
由于水质数据特征复杂、关联度参差不齐而导致溶解氧浓度预测难度较大,为提高水质溶解氧浓度预测的准确性,提出了一种基于特征工程和北方苍鹰优化算法的长短期记忆网络(Feature Engineering-Northern Goshawk Optimization-Long Short T...由于水质数据特征复杂、关联度参差不齐而导致溶解氧浓度预测难度较大,为提高水质溶解氧浓度预测的准确性,提出了一种基于特征工程和北方苍鹰优化算法的长短期记忆网络(Feature Engineering-Northern Goshawk Optimization-Long Short Term Memory,FE-NGO-LSTM)混合模型。首先对水质数据集进行缺失值补齐、特征筛选与特征多项式构造,然后基于NGO-LSTM模型优化模型参数,提升预测性能;对不同多项式阶数下的特征预测效果进行分析之后,将该模型与基于灰狼优化算法、鲸鱼优化算法及粒子群优化算法的LSTM模型进行对比;最后,在太湖流域东苕溪城南监测断面对该模型进行了验证,计算FE-NGO-LSTM模型预见期为4,8,12,16,20,24 h的预测结果。试验结果显示:当多项式阶数为2阶时,模型预测效果最好,FE-NGO-LSTM模型相比基于其他优化算法的LSTM模型,平均绝对误差、均方误差、均方根误差分别至少降低9.0%,12.9%及6.3%,且随着预见期的增加,预测误差仍在可接受范围内,说明FE-NGO-LSTM模型在预测溶解氧浓度时具有一定优势与泛化性。展开更多
研究以太湖区域中林地、桃园、稻田以及链接这些典型土地的河流共8个点为取样地,借助16S r RNA扩增子高通量测序和分析技术,基于Illumina Hi Seq测序平台,利用双末端测序的方法得到1 865 862条原始序列,通过对原始序列进行拼接、过滤,得...研究以太湖区域中林地、桃园、稻田以及链接这些典型土地的河流共8个点为取样地,借助16S r RNA扩增子高通量测序和分析技术,基于Illumina Hi Seq测序平台,利用双末端测序的方法得到1 865 862条原始序列,通过对原始序列进行拼接、过滤,得到1 712 497条有效序列,按照97%相似性将优质序列划分为95 132条可操作分类单元(OTU,operational taxonomic units),同时基于OTUs信息计算Alpha和Beta群落丰富度和多样性指数,并采用RDP Classifier结合Green Gene数据库对代表性OTU序列进行注释。在OTU聚类信息、群落多样性指数及组间差异性物种统计方面,分析了明显连续降雨后监测的细菌群落从农田到河流连续地变化规律。结果表明,森林景观带、稻田、桃园土壤细菌种群多样性明显比河流水体中丰富,说明陆源微生物在迁移过程中发生了适应特定生境的变化。Acidobacteria门在森林景观带、水稻田、桃园中表现出较高的丰度,Proteobacteria门在河流中展现出更大的种群优势。Nitrospirae门是一类具有硝化作用的细菌,在水稻田中显示较高的优势。Yersinia属、Flavobacterium属、Aeromonas属、Pseudomonas属、Acinetobacter属是具有传播水生动物、人畜流行疾病能力的种群,并且在河流中具有较高丰度,应当引起注意。Sphaerotilus属对污水中的有机物和有毒物质有很强的降解作用,在河流中的丰度高于土壤。Sphingomonas属、Arthrobacter属、Thiobacillus属能够降解除草剂、农药等有机污染物,在桃园和稻田中表现出较高丰度,具有环境修复价值。本文为系统掌握太湖流域果园和水稻田土壤细菌群落结构的差异特征,以及土壤微生物群落从农田系统到河流的变化规律提供了理论依据,提示该区域农业从事者应当从经济、生态和健康方面综合考虑土地利用方式。展开更多
基金supported by State Key Development Program of Basic Research of China (Grant No.2010CB429001)the National Natural Science Foundation of China (Grant No. 51009062)the Special Fund of Hydrology-Water Resources and Hydraulic Engineering (Grant No. 2009586812)
文摘To meet the increasing ：need of fresh water and to improve the water quality of Taihu Lake, water transfer from the Yangtze River was initiated in 2002. This study was performed to investigate the sediment distribution along the river course following water transfer. A rainfall-runoff model was first built to calculate the runoff of the Taihu Basin in 2003. Then, the flow patterns of river networks were simulated using a one-dimensional river network hydrodynamic model. Based on the boundary conditions of the flow in tributaries of the Wangyu River and the water level in Taihu Lake, a one-dimensional hydrodynamic and sediment transport numerical model of the Wangyu River was built to analyze the influences of the inflow rate of the water transfer and the suspended sediment concentration （SSC） of inflow on the sediment transport. The results show that the water transfer inflow rate and SSC of inflow have significant effects on the sediment distribution. The higher the inflow rate or SSC of inflow is, the higher the SSC value is at certain cross-sections along the ：river course of water transfer. Higher inflow rate and SSC of inflow contribute to higher sediment deposition per kilometer and sediment thickness. It is also concluded that a sharp decrease of the inflow velocity at the entrance of the Wangyu River on the river course of water transfer induces intense sedimentation at the cross-section near the Changshu hydro-junction. With an increasing distance from the Changshu hydro-junction, the sediment deposition and sedimentation thickness decrease gradually along the river course.
文摘由于水质数据特征复杂、关联度参差不齐而导致溶解氧浓度预测难度较大,为提高水质溶解氧浓度预测的准确性,提出了一种基于特征工程和北方苍鹰优化算法的长短期记忆网络(Feature Engineering-Northern Goshawk Optimization-Long Short Term Memory,FE-NGO-LSTM)混合模型。首先对水质数据集进行缺失值补齐、特征筛选与特征多项式构造,然后基于NGO-LSTM模型优化模型参数,提升预测性能;对不同多项式阶数下的特征预测效果进行分析之后,将该模型与基于灰狼优化算法、鲸鱼优化算法及粒子群优化算法的LSTM模型进行对比;最后,在太湖流域东苕溪城南监测断面对该模型进行了验证,计算FE-NGO-LSTM模型预见期为4,8,12,16,20,24 h的预测结果。试验结果显示:当多项式阶数为2阶时,模型预测效果最好,FE-NGO-LSTM模型相比基于其他优化算法的LSTM模型,平均绝对误差、均方误差、均方根误差分别至少降低9.0%,12.9%及6.3%,且随着预见期的增加,预测误差仍在可接受范围内,说明FE-NGO-LSTM模型在预测溶解氧浓度时具有一定优势与泛化性。
