Interrupting a multi-species bioinvasion vector: the efficacy of in-water cleaning for removing biofouling on obsolete vessels

Vector management is the primary method for reducing and preventing nonindigenous species (NIS) invasions and their ecological and economic consequences. This study was the first to examine the efficacy of in-water scrubbing using a submersible cleaning and maintenance platform (SCAMP) to prevent invertebrate species transfers from a heavily fouled obsolete vessel. Initially, prior to treatment, 37 species were recorded in a biofouling matrix that reached 30cm depth in some locations. The bryozoan Conopeum chesapeakensis, and bivalves Mytilopsis leucophaeata and Ischadium recurvum, were dominant sessile species that created structure, supporting mobile biota that included crabs and the associated parasitic barnacle Loxothylacus panopae. Scrubbing had the effect of significantly reducing organism extent and the number of species per sample, but a substantial and diverse (30 species) residual fouling community remained across the entire vessel. Further assessments of management options are needed to prevent potentially damaging NIS transfers. Additional measures taken within an integrated vector management (IVM) strategy may further improve invasion prevention measures.     

Probabilistic reservoir operation using Bayesian stochastic model and support vector machine

Available water resources are often not sufficient or too polluted to satisfy the needs of all water users. Therefore, allocating water to meet water demands with better quality is a major challenge in reservoir operation. In this paper, a methodology to develop operating strategies for water release from a reservoir with acceptable quality and quantity is presented. The proposed model includes a genetic algorithm (GA)-based optimization model linked with a reservoir water quality simulation model. The objective function of the optimization model is based on the Nash bargaining theory to maximize the reliability of supplying the downstream demands with acceptable quality, maintaining a high reservoir storage level, and preventing quality degradation of the reservoir. In order to reduce the run time of the GA-based optimization model, the main optimization model is divided into a stochastic and a deterministic optimization model for reservoir operation considering water quality issues.The operating policies resulted from the reservoir operation model with the water quantity objective are used to determine the released water ranges (permissible lower and upper bounds of release policies) during the planning horizon. Then, certain values of release and the optimal releases from each reservoir outlet are determined utilizing the optimization model with water quality objectives. The support vector machine (SVM) model is used to generate the operating rules for the selective withdrawal from the reservoir for real-time operation. The results show that the SVM model can be effectively used in determining water release from the reservoir. Finally, the copula function was used to estimate the joint probability of supplying the water demand with desirable quality as an evaluation index of the system reliability. The proposed method was applied to the Satarkhan reservoir in the north-western part of Iran. The results of the proposed models are compared with the alternative models. The results show that the proposed models could be used as effective tools in reservoir operation.     

地震动作用下黄土边坡稳定性分析

以甘肃省内两种典型的高速公路沿线边坡作为研究对象,利用有限元数值模拟计算分析了不同地震动对支护边坡和无支护边坡稳定性的影响.计算结果表明,随着地震动峰值加速度的增大边坡安全系数逐渐降低;支护不但有助于提高边坡自身的稳定性,而且有利于增强边坡坡体的抗震性能.边坡支护设计强度主要依照当地可能遭受的最大地震动计算,以便最大限度地发挥支护提高边坡稳定性的效率.     

A new approach for crude oil price prediction based on stream learning

Crude oil is the world's leading fuel, and its prices have a big impact on the global environment, economy as well as oil exploration and exploitation activities. Oil price forecasts are very useful to industries, governments and individuals. Although many methods have been developed for predicting oil prices, it remains one of the most challenging forecasting problems due to the high volatility of oil prices. In this paper, we propose a novel approach for crude oil price prediction based on a new machine learning paradigm called stream learning. The main advantage of our stream learning approach is that the prediction model can capture the changing pattern of oil prices since the model is continuously updated whenever new oil price data are available, with very small constant overhead. To evaluate the forecasting ability of our stream learning model, we compare it with three other popular oil price prediction models. The experiment results show that our stream learning model achieves the highest accuracy in terms of both mean squared prediction error and directional accuracy ratio over a variety of forecast time horizons.     

Measures to diminish the parameter drift in the modeling of ship manoeuvring using system identification

System identification provides an effective way to predict the ship manoeuvrability. In this paper several measures are proposed to diminish the parameter drift in the parametric identification of ship manoeuvring models. The drift of linear hydrodynamic coefficients can be accounted for from the point of view of dynamic cancellation, while the drift of nonlinear hydrodynamic coefficients is explained from the point of view of regression analysis. To diminish the parameter drift, reconstruction of the samples and modification of the mathematical model of ship manoeuvring motion are carried out. Difference method and the method of additional excitation are proposed to reconstruct the samples. Using correlation analysis, the structure of a manoeuvring model is simplified. Combined with the measures proposed, support vector machines based identification is employed to determine the hydrodynamic coefficients in a modified Abkowitz model. Experimental data from the free-running model tests of a KVLCC2 ship are analyzed and the hydrodynamic coefficients are identified. Based on the regressive model, simulation of manoeuvres is conducted. Comparison between the simulation results and the experimental results demonstrates the validity of the proposed measures.     

Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China

The Support Vector Machine (SVM) is an increasingly popular learning procedure based on statistical learning theory, and involves a training phase in which the model is trained by a training dataset of associated input and target output values. The trained model is then used to evaluate a separate set of testing data. There are two main ideas underlying the SVM for discriminant-type problems. The first is an optimum linear separating hyperplane that separates the data patterns. The second is the use of kernel functions to convert the original non-linear data patterns into the format that is linearly separable in a high-dimensional feature space. In this paper, an overview of the SVM, both one-class and two-class SVM methods, is first presented followed by its use in landslide susceptibility mapping. A study area was selected from the natural terrain of Hong Kong, and slope angle, slope aspect, elevation, profile curvature of slope, lithology, vegetation cover and topographic wetness index (TWI) were used as environmental parameters which influence the occurrence of landslides. One-class and two-class SVM models were trained and then used to map landslide susceptibility respectively. The resulting susceptibility maps obtained by the methods were compared to that obtained by the logistic regression (LR) method. It is concluded that two-class SVM possesses better prediction efficiency than logistic regression and one-class SVM. However, one-class SVM, which only requires failed cases, has an advantage over the other two methods as only “failed” case information is usually available in landslide susceptibility mapping.     

土地利用动态遥感监测数据组织模式研究

遥感技术为土地利用动态调查提供客观、快速的时空信息,但是,目前国内还无有效的方法和模型对遥感监测结果进行组织,从而限制了遥感监测数据应用的深度和广度.本文采用分段编码、分层存储的数据文件组织方式,利用转移矩阵作为变化信息的组织模式,基于SVG(Scalable Vector Graphics)的Path技术提取分层图斑边界,并将图斑的矢量和属性数据整合于CTG(Composite Theme Grid)格式的开放文件中,方便了数据的综合查询,提高了数据的使用广度和深度.     

耀斑与磁环拓扑界面

北京天文台怀柔太阳观测站和美国大熊湖太阳天文台的矢量磁图系列，已经允许从观测上证认活动区内的磁环系统，及磁环之间的拓扑界面。本文作者基于１９９３年４月６日至１２日，对ＮＯＡＡ７４６９连续的矢量磁场观测，首次对一个活动区内的磁环系统和磁环之间的拓扑界面作了观测证认，这些拓扑界面的特征是：１）矢量磁场在界面附近高度剪切；２）在界面两边，有很强的纵场梯度（约０．１高斯／公里）；３）在观面两边磁对消，对消     

基于图形的测量控制点管理系统

测量控制点成果是国土、房管、规划、测绘等行业的重要资料,通常包括“一图两表”,即控制网略图、控制点成果表、点之记。目前,对测量控制点成果的管理模式大多是控制点成果表与控制网图形、点之记相分离的手工管理,对其进行一体化的科学管理是一个重要课题。为了实现测量控制点成果的信息化管理,作者不仅进行了业务调查、业务分析、系统设计,而且实现了测量控制点管理系统的建设,该系统不仅具有通用MIS系统的功能,而且具有与属性有机结合的图形功能。     

自来水管线符号库的制作及扫描矢量化

随着地理信息系统(Geograph ic Information System,简称G IS)的飞速发展和数字地球的提出,空间数据在诸如航空、遥感、测绘、交通、水电等不同领域展现出日益强大的生命力。空间数据采集的手段有很多,纸制图的数字化是G IS中空间数据采集的主要手段之一。结合广州市白云区和海珠区自来水管网矢量化生产实践,介绍了用ArcG IS完成自来水管线图扫描矢量化的整个过程,包括图象处理、配准、数字化、检查、图形接边等各个工序的实现方法,其中着重介绍了符号库的制作和地图接边及检查等技术问题。实践表明,该方法是一种简便、实用的扫描矢量化方法。