Sediment load estimation is generally required for study and development of water resources system. In this regard, artificial neural network (ANN) is the most widely used modeling tool especially in data-constraint r...Sediment load estimation is generally required for study and development of water resources system. In this regard, artificial neural network (ANN) is the most widely used modeling tool especially in data-constraint regions. This research attempts to combine SSA (singular spectrum analysis) with ANN, hereafter called SSA-ANN model, with expectation to improve the accuracy of sediment load predicted by the existing ANN approach. Two different catchments located in the Lower Mekong Basin (LMB) were selected for the study and the model performance was measured by several statistical indices. In comparing with ANN, the proposed SSA-ANN model shows its better performance repeatedly in both catchments. In validation stage, SSA-ANN is superior for larger Nash-Sutcliffe Efficiency about 24% in Ban Nong Kiang catchment and 7% in Nam Mae Pun Luang catchment. Other statistical measures of SSA-ANN are better than those of ANN as well. This improvement reveals the importance of SSA which filters noise containing in the raw time series and transforms the original input data to be near normal distribution which is favorable to model simulation. This coupled model is also recommended for the prediction of other water resources variables because extra input data are not required. Only additional computation, time series decomposition, is needed. The proposed technique could be potentially used to minimize the costly operation of sediment measurement in the LMB which is relatively rich in hydrometeorological records.展开更多
In order to effectively cope with exponent increase of the complexity faced to the rock mechanics analysis problems and the large incompatibility existing between the information level required to model the rock mass ...In order to effectively cope with exponent increase of the complexity faced to the rock mechanics analysis problems and the large incompatibility existing between the information level required to model the rock mass and engineering and our obtainable information level at hand,the integrated approaches with intelligent characters are proposed. Many previous standard methods,such as precedent type analysis,rock classification,analytic method stress-based,basic numerical methods (BEM,FEM,DEM,hybrid),and their extended numerical methods (fully coupled) to be developed,can be selected respectively or integrated accordingly. It is alternative to develop basic/fully integrated system,and internet-based approaches. These novel methods can also be selected or integrated each other or with the standard methods to perform rock mechanics analysis. Some key techniques to develop these alternative methods are discussed. It may focus in future on developing fully integrated systems and internet-based approaches. Developing an environmental,virtual facility/space shall be firstly done for this collaborative research on internet.展开更多
食物网结构特征和能量流动的研究,对于维持海洋生态系统结构和功能的稳定具有重要意义,有助于深入理解海洋生态系统的复杂过程。本研究基于2019-2021年在江苏近海北部海域开展的季节性渔业资源底拖网调查数据,通过构建基于蒙特卡罗马尔...食物网结构特征和能量流动的研究,对于维持海洋生态系统结构和功能的稳定具有重要意义,有助于深入理解海洋生态系统的复杂过程。本研究基于2019-2021年在江苏近海北部海域开展的季节性渔业资源底拖网调查数据,通过构建基于蒙特卡罗马尔科夫链算法的逆线性模型(Linear Inverse Models using a Monte Carlo Method Coupled with Markov Chain, LIM-MCMC),结合生态网络分析(Ecological Network Analysis,ENA)的方法,分析了该海域生态系统状态和食物网能量流动特征,旨在为江苏近海北部海域食物网营养动力学研究提供参考依据。结果表明,该海域生态系统共包含299条能量流动路径,能量流动分布整体呈典型的金字塔结构,各功能群呼吸消耗和流入有机碎屑的能量保持同步性。通过与其他海域比较发现,江苏近海北部海域生态系统的连接指数(Connectance,C)和系统杂食指数(System Omnivory Index,SOI)分别为0.40和0.22,处于较高水平,表明该生态系统不同营养级间的营养联系较为紧密,食物网结构相对复杂,能够在较大程度上抵御外界扰动。总初级生产力/总呼吸(Total Primary Production/Total Respiration,TPP/TR)和Finn’s循环指数(Finn’s Cycling Index,FCI)分别为1.05和5.76%,表明该生态系统对能量利用效率较高。此外,约束效率(Constraint Efficiency,CE)、发展程度(Extent of Development,AC)、协同效应指数(Synergism Index,b/c)和主导间接效应(Dominance Indirect Effects,i/d)也表明该生态系统具有较高的系统发展程度、再生潜力和系统发展空间。本研究将有助于为江苏近海北部海域生态系统的修复和渔业资源的可持续利用提供理论基础,为实施基于生态系统的渔业管理提供科学依据。展开更多
文摘Sediment load estimation is generally required for study and development of water resources system. In this regard, artificial neural network (ANN) is the most widely used modeling tool especially in data-constraint regions. This research attempts to combine SSA (singular spectrum analysis) with ANN, hereafter called SSA-ANN model, with expectation to improve the accuracy of sediment load predicted by the existing ANN approach. Two different catchments located in the Lower Mekong Basin (LMB) were selected for the study and the model performance was measured by several statistical indices. In comparing with ANN, the proposed SSA-ANN model shows its better performance repeatedly in both catchments. In validation stage, SSA-ANN is superior for larger Nash-Sutcliffe Efficiency about 24% in Ban Nong Kiang catchment and 7% in Nam Mae Pun Luang catchment. Other statistical measures of SSA-ANN are better than those of ANN as well. This improvement reveals the importance of SSA which filters noise containing in the raw time series and transforms the original input data to be near normal distribution which is favorable to model simulation. This coupled model is also recommended for the prediction of other water resources variables because extra input data are not required. Only additional computation, time series decomposition, is needed. The proposed technique could be potentially used to minimize the costly operation of sediment measurement in the LMB which is relatively rich in hydrometeorological records.
基金Nature Science Foundation of China under Grant no.50179034.
文摘In order to effectively cope with exponent increase of the complexity faced to the rock mechanics analysis problems and the large incompatibility existing between the information level required to model the rock mass and engineering and our obtainable information level at hand,the integrated approaches with intelligent characters are proposed. Many previous standard methods,such as precedent type analysis,rock classification,analytic method stress-based,basic numerical methods (BEM,FEM,DEM,hybrid),and their extended numerical methods (fully coupled) to be developed,can be selected respectively or integrated accordingly. It is alternative to develop basic/fully integrated system,and internet-based approaches. These novel methods can also be selected or integrated each other or with the standard methods to perform rock mechanics analysis. Some key techniques to develop these alternative methods are discussed. It may focus in future on developing fully integrated systems and internet-based approaches. Developing an environmental,virtual facility/space shall be firstly done for this collaborative research on internet.
文摘食物网结构特征和能量流动的研究,对于维持海洋生态系统结构和功能的稳定具有重要意义,有助于深入理解海洋生态系统的复杂过程。本研究基于2019-2021年在江苏近海北部海域开展的季节性渔业资源底拖网调查数据,通过构建基于蒙特卡罗马尔科夫链算法的逆线性模型(Linear Inverse Models using a Monte Carlo Method Coupled with Markov Chain, LIM-MCMC),结合生态网络分析(Ecological Network Analysis,ENA)的方法,分析了该海域生态系统状态和食物网能量流动特征,旨在为江苏近海北部海域食物网营养动力学研究提供参考依据。结果表明,该海域生态系统共包含299条能量流动路径,能量流动分布整体呈典型的金字塔结构,各功能群呼吸消耗和流入有机碎屑的能量保持同步性。通过与其他海域比较发现,江苏近海北部海域生态系统的连接指数(Connectance,C)和系统杂食指数(System Omnivory Index,SOI)分别为0.40和0.22,处于较高水平,表明该生态系统不同营养级间的营养联系较为紧密,食物网结构相对复杂,能够在较大程度上抵御外界扰动。总初级生产力/总呼吸(Total Primary Production/Total Respiration,TPP/TR)和Finn’s循环指数(Finn’s Cycling Index,FCI)分别为1.05和5.76%,表明该生态系统对能量利用效率较高。此外,约束效率(Constraint Efficiency,CE)、发展程度(Extent of Development,AC)、协同效应指数(Synergism Index,b/c)和主导间接效应(Dominance Indirect Effects,i/d)也表明该生态系统具有较高的系统发展程度、再生潜力和系统发展空间。本研究将有助于为江苏近海北部海域生态系统的修复和渔业资源的可持续利用提供理论基础,为实施基于生态系统的渔业管理提供科学依据。
