Performance comparison of reduced models for leak detection in water distribution networks

This paper presents a methodology for comparing the performance of model-reduction strategies to be used with a diagnostic methodology for leak detection in water distribution networks. The goal is to find reduction strategies that are suitable for error-domain model falsification, a model based data interpretation methodology. Twelve reduction strategies are derived from five strategy categories. Categories differ according to the manner in which nodes are selected for deletion. A node is selected for deletion according to: (1) the diameter of the pipes; (2) the number of pipes linked to a node; (3) the angle of the pipes in the case of two-pipe nodes; (4) the distribution of the water demand; and, (5) a pair-wise combination of some categories.The methodology is illustrated using part of a real network. Performance is evaluated first by judging the equivalency of the reduced network with the initial network (before the application of any reduction procedure) and secondly, by assessing the compatibility with the diagnostic methodology. The results show that for each reduction strategy the equivalency of networks is verified. Computational time can be reduced to less than 20% of the non-reduced network in the best case. Results of diagnostic performance show that the performance decreases when using reduced networks. The reduction strategy with the best diagnostic performance is that based on the angle of two-pipe nodes, with an angle threshold of 165°. In addition, the sensitivity of the performance of the reduced networks to variation in leak intensity is evaluated. Results show that the reduction strategies where the number of nodes is significantly reduced are the most sensitive.Finally this paper describes a Pareto analysis that is used to select the reduction strategy that is a good compromise between reduction of computational time and performance of the diagnosis. In this context, the extension strategy is the most attractive.     

Comparing lazy and eager learning models for water level forecasting in river-reservoir basins of inundation regions

This study developed a methodology for formulating water level models to forecast river stages during typhoons, comparing various models by using lazy and eager learning approaches. Two lazy learning models were introduced: the locally weighted regression (LWR) and the k-nearest neighbor (kNN) models. Their efficacy was compared with that of three eager learning models, namely, the artificial neural network (ANN), support vector regression (SVR), and linear regression (REG). These models were employed to analyze the Tanshui River Basin in Taiwan. The data collected comprised 50 historical typhoon events and relevant hourly hydrological data from the river basin during 1996–2007. The forecasting horizon ranged from 1 h to 4 h. Various statistical measures were calculated, including the correlation coefficient, mean absolute error, and root mean square error. Moreover, significance, computation efficiency, and Akaike information criterion were evaluated. The results indicated that (a) among the eager learning models, ANN and SVR yielded more favorable results than REG (based on statistical analyses and significance tests). Although ANN, SVR, and REG were categorized as eager learning models, their predictive abilities varied according to various global learning optimizers. (b) Regarding the lazy learning models, LWR performed more favorably than kNN. Although LWR and kNN were categorized as lazy learning models, their predictive abilities were based on diverse local learning optimizers. (c) A comparison of eager and lazy learning models indicated that neither were effective or yielded favorable results, because the distinct approximators of models that can be categorized as either eager or lazy learning models caused the performance to be dependent on individual models.     

基于软构件的Web办公系统的设计与实现

该文对软构件进行了研究,提出了利用DSCIE（Distributed Software Component Integrated Environment）,即分布式软构件集成环境的框架开发Web办公系统的方法。Web办公系统通过.net技术来实现。并且基于这个框架成功开发出了铁路Web办公系统。     

南水北调南干渠工程穿越风险地段的施工方法

南水北调南干渠Ⅲ标需要全段穿越大兴灌渠、桥墩、垃圾坑以及二元地质结构地层等风险地段,施工难度大.施工中采取了铺设土工膜、现浇混凝土方涵导流、加密钢格栅等方法,安全地穿越了风险地段.     

宜春市状元洲抬水工程设计浅析

以江西省宜春市状元洲抬水工程为实例,探讨了橡胶坝及升卧式闸门在抬水工程中的设计及应用.     

引气剂在南水北调中线工程中的应用

引气剂在混凝土中能够引入大量微小、封闭、稳定的气泡,可改善混凝土和易性、耐久性,延长建筑物的使用寿命。引气剂的使用效果与混凝土的原材料质量、拌和工艺及环境有关,使用时应适当调整掺量,满足含气量的技术指标要求。     

Eclipse插件的开发与研究

本文以Eclipse插件为例,给出了插件开发的目的、意义和具体实现的方法。     

油气系统工程从系统工程角度看油气业的安全和环境保护

本文从系统工程的角度对石油天然气工业的整体作系统分析,并从资源和环境的挑战和机遇进行讨论,强调其中资源是关键的,它是客观存在的,条件是困难的,需要不断投入,加强研究,努力创新。强调建立健康安全环境HSE体系的重要性和必要性,同时给出墨西哥湾BP深水地平线平台事故造成严重后果的案例。最后给出了简单结论-如何面对和适宜采取的措施,如面对一个油气工程系统必须作好系统分析,用系统工程的办法去解决,要以人为本,从上层至基层,注意人为因素,严格完整地采用和发展HSE体系的作用无疑是极为有效的方法。     

Spacebased estimation of moisture transport in marine atmosphere using support vector regression

An improved algorithm is developed based on support vector regression (SVR) to estimate horizonal water vapor transport integrated through the depth of the atmosphere (Θ) over the global ocean from observations of surface wind-stress vector by QuikSCAT, cloud drift wind vector derived from the Multi-angle Imaging SpectroRadiometer (MISR) and geostationary satellites, and precipitable water from the Special Sensor Microwave/Imager (SSM/I). The statistical relation is established between the input parameters (the surface wind stress, the 850 mb wind, the precipitable water, time and location) and the target data (Θ calculated from rawinsondes and reanalysis of numerical weather prediction model). The results are validated with independent daily rawinsonde observations, monthly mean reanalysis data, and through regional water balance. This study clearly demonstrates the improvement of Θ derived from satellite data using SVR over previous data sets based on linear regression and neural network. The SVR methodology reduces both mean bias and standard deviation compared with rawinsonde observations. It agrees better with observations from synoptic to seasonal time scales, and compare more favorably with the reanalysis data on seasonal variations. Only the SVR result can achieve the water balance over South America. The rationale of the advantage by SVR method and the impact of adding the upper level wind will also be discussed.