基于遗传算法的生命线工程网络抗震优化设计

进行网络抗震可靠性分析的目的,不仅在于定量评价生命线工程网络系统的抗震性能,更重要的是利用这种分析工具指导网络抗震性能的优化设计。以无向边权网络系统为分析对象,分别以管网造价和系统抗震可靠度作为优化目标和约束条件,建立网络系统拓扑优化模型,利用递推分解算法作为网络系统抗震可靠性分析工具,并引入系统单元的灵敏度分析,采用遗传算法求解网络系统的拓扑优化问题,从而发展了一类工程网络抗震优化设计方法。实例分析结果表明,网络系统拓扑结构与系统可靠度之间有显著的正相关关系。     

生命线工程网络抗震可靠性分析方法的比较研究

生命线工程网络抗震可靠性分析是进行生命线工程系统规划、设计、改造与优化的基础。因此 ,寻找一种高效的适应性强的系统可靠性分析方法对实际工程应用具有重要的意义。本文主要介绍两种高效的网络系统两终端连通可靠性分析方法———递推分解算法和有序二分决策图算法。首先 ,分别对两种算法的原理及实现过程进行了详细的论述和说明 ,并且用c语言编制了二分决策图算法的计算程序。然后 ,利用这两种方法及随机模拟算法对 2 0个以往研究中的经典算例和两个实际工程网络———河南省电力网和上海市浦西供水管网 ,进行了网络系统抗震连通可靠性分析。通过对实例分析结果的比较研究 ,得到了一些经验性的结论 ,以期为优化设计工程网络时选择系统可靠性的分析工具提供参考。研究表明 ,由于递推分解算法具有适应性强和能够求得问题近似解的优点 ,因此有良好的实际工程应用前景。     

多层递阶方法在变化环境下非一致性水文序列分析中的应用

由于受人类活动、气候等外界变化环境的影响,用于水资源评价计算的天然年径流量序列失去了一致性。为了适应变化环境对水文预报的要求,提出了基于时变参数的非一致性年径流序列的多层递阶预报方法。该方法不同于传统带固定参数的模型,它是将预报对象看作为随机动态时变系统,首先进行动态系统的时变参数预报,进而在参数预报基础之上对系统状态预报,从而提高了预测结果的可靠性。以闽江竹岐水文站为例,进行年径流量的中长期预报,为区域水资源的优化管理提供科学的依据。     

VALUATION OF IMPROVED IRRIGATION EFFICIENCY FROM AN EXHAUSTIBLE GROUNDWATER SOURCE1

This paper presents estimates made via a recursive linear programming model of the net benefits of improving irrigation application efficiency from an exhaustible groundwater source. Net benefits were derived for different application efficiency levels under furrow, sprinkler, and LEPA irrigation systems. In addition, net benefit estimates were obtained for the transition across irrigation systems. Solutions from the model indicate that low crop prices have a differential impact on net benefits across irrigation application efficiencies and irrigation systems. Also, the more limited groundwater situations consistently reduce the economic incentive to adopt improved irrigation application techniques across all irrigation systems.     

KALMAN FILTER ESTIMATION AND PREDICTION OF DAILY STREAM FLOWS: I. REVIEW,ALGORITHM, AND SIMULATION EXPERIMENTS1

An important class of models, frequently used in hydrology for the forecasting of hydrologic variables one or more time periods ahead, or for the generation of synthetic data sequences, is the class of autoregressive(AR) models. As the AR models belong to the family of linear stochastic difference equations, they have both a deterministic and a stochastic component. The stochastic component is often assumed to have a Gaussian distribution. It is well known that hydrologic observations (e.g., stream flows) are heavily affected by noise. To account explicitly for the observation noise, the linear stochastic difference equation is expressed in state variable form and an observation model is introduced. The discrete Kalman filter algorithm can then be used to obtain estimates of the state variable vector. Typically, in hydrologic systems, model parameters, system noise statistics and measurement noise statistics are unknown, and have to be estimated. In this study an adaptive algorithm is discussed which estimates these quantities simultaneously with the state variables. The performance of the algorithm is evaluated by using simulated data.     

非定向河网水质模式及计算方法

在研究京津地区排污河网有机污染物的分布时,建立了非定向河网水质模式,并在计算中引入了相关矩阵和递归函数的概念,使模式的计算过程简化,解决了由于河水流向不定带来的问题。     

千岛湖地区上梧溪流域基流磷输出定量研究

基于双参数递归数字滤波 （ERDF）、LOADEST模型和遗传算法,建立一种递归滤波基流负荷分割算法 （RFLSA）,对千岛湖地区上梧溪流域的基流总磷 （TP） 负荷进行分割定量.结果表明：利用遗传算法对双参数滤波日尺度退水常数和最大基流指数 （BFI_max） 进行同步优化,可以有效提高ERDF基流分割结果的准确性和可靠性 （NSE = 0.92, RSR = 0.29, R² = 0.92）;以此为基础建立的RFLSA能够实现上梧溪流域基流TP负荷的准确定量 （NSE = 0.79, RSR = 0.46, R² = 0.95）,可以作为流域尺度上基流非点源污染定量评价的一种有效方法;2020年11月—2021年10月,上梧溪流域以基流形式输出的TP负荷量为0.167 kg·hm^-2,占总径流负荷量 （0.302 kg·hm^-2） 的比例高达55.30%.基流已经成为上梧溪流域非点源TP的主要输出途径,是该地区地表河流水体一个不容忽视的重要污染源.     

Spatial Considerations in Wet and Dry Periods for Phosphorus in Streams of the Fort Cobb Watershed,United States

The Fort Cobb Watershed in Oklahoma has diverse biogeophysical settings and provides an opportunity to explore the association of water quality with a diverse set of landscapes during both wet (April 2007‐December 2009) and dry (January 2005‐March 2007) periods. The objective of this work was to identify spatial patterns in phosphorus (P) (soluble reactive P [SRP] and bioavailable P [BAP]) associated with landscape metrics for two distinct streamflow regimes. Spatial autocorrelation of P was evaluated using contiguous (side‐by‐side) and upstream (upstream:downstream) connectivity matrices. Biogeophysical metrics were compiled for each contributing area, and were partitioned based on association to P concentrations. Results for both SRP and BAP indicated that spatial autocorrelation was present (p < 0.05). There was more spatial autocorrelation and stream P concentrations were three to five times higher in the Wet phase than in the Dry phase (p < 0.05). Analysis with recursive partitioning resulted in higher R² with spatial autocorrelation than without spatial autocorrelation and indicated that lateral metrics (topography, soil, geology, management) were better predictors for SRP than instream metrics. During Wet phase, lateral metrics indicative of rapid surface and subsurface water movement were associated with higher P stream concentrations. This research demonstrated that we can detect landscapes more vulnerable to P losses and/or contaminations in either drought or very wet periods.     

徐州市城市垃圾综合处理系统研究

针对徐州市城市垃圾污染及其处理情况，运用线性回归法对垃圾产生量进行了定量预测，用层次分析法优选了适合于该市的城市垃圾综合处理系统，从而因地制宜地提出了适合于该城市垃圾处理的方案。     

KALMAN FILTER ESTIMATION AND PREDICTION OF DAILY STREAM FLOWS: II. APPLICATION TO THE POTOMAC RIVER1

Results are reported from an application of the state space formulation and the Kalman filter to real-time forecasting of daily river flows. It is shown that the application of filtering techniques improves the overall forecasting performance of the model. As is true for most hydrologic systems, the model is not completely known. Therefore, the procedures pertaining to on-line parameter and noise statistics estimation, as presented in the first paper, are implemented. The example in this paper shows that these techniques also perform satisfactorily when applied to a real-world situation.