水中溶解氧含量的优化WLS-SVM预测方法

研究溶解氧含量预测的精确度与鲁棒性问题,为水质评价和水处理控制提供科学依据.探讨了水中溶解氧的影响因素及其作用规律,分析了现有预测算法的不足,在此基础上提出了一种新的溶解氧含量预测方法.从样本集中随机抽取数据构成训练集和测试集,以网格搜索法确定WLS-SVM的参数寻优范围,再用QPSO与留一交叉验证组合算法找出其最优值,以此建立WLS-SVM回归模型进行水中溶解氧含量的预测.应用该方法与LS-SVMlab工具箱函数分别建模进行对比测试,结果表明其预测精确度和鲁棒性都更好.     

Environmental risk assessment of offshore produced water discharges using a hybrid fuzzy-stochastic modeling approach

In recent years, regulators and environmental groups have identified the large volumes of wastewater discharged through offshore petroleum production activities as an issue of concern. In this paper, fuzzy set theory coupled with Monte Carlo analysis have provided a stochastic simulation of pollutant dispersion for the prediction of the environmental risks associated with produced water discharges. With the application of the fuzzy set to data drawn from previous risk-assessment studies, the model allowed the evaluation of various existing pollution standards for marine environments. The present modeling method was validated against data for lead (Pb) levels in the area adjacent to an offshore petroleum facility located on the Grand Banks of Newfoundland, Canada through a multi-year field expedition. The proposed risk-assessment approach contributes to the implementation of effective assessment and management of produced water discharges in the offshore water environment.     

Developing an intelligent expert system for streamflow prediction,integrated in a dynamic decision support system for managing multiple reservoirs: A case study

Since fresh water is limited while agricultural and human water demands are continuously increasing, optimal prediction and management of streamflows as a source of fresh water is crucially important. This study investigates and demonstrates how data preprocessing and data mining techniques would improve the accuracy of streamflow predictive models. Based on easily accessible Snow Telemetry data (SNOTEL), four streamflow prediction models – autoregressive integrated moving average (ARIMA), artificial neural networks (ANNs), a hybrid-model of ANN and ARIMA (ANN-ARIMA), and an adaptive neuro fuzzy inference system (ANFIS) – were developed and utilized in a streamflow prediction process on Elephant Butte Reservoir. Utilizing the statistical correlation analysis and the extracting importance degrees of predictors led to efficiently select the most effective predictors for daily and monthly streamflow to Elephant Butte Reservoir. For the daily prediction time step, by preprocessing the historical data and extracting and utilizing the extracted climate variability indices through data mining techniques, the ANFIS model achieved a superior streamflow prediction performance for Elephant Butte Reservoir compared to the other three evaluated prediction models. Additionally, for predicting monthly streamflow to the Elephant Butte Reservoir, ANFIS showed significantly higher accuracy than the ANNs. As an optimal application of the developed predictive expert systems, successful integrating the prediction models in integrated reservoir operations balanced the need for a reliable supply of irrigation water against losses through evaporation. The optimal operation plan significantly minimizes the total evaporation loss from both reservoirs by providing the optimal storage levels in both reservoirs. This study provides the conceptual procedures of non-seasonal (ARIMA) model, and since the model is univariate, it demonstrates a strongly-reliable inflow prediction when existing information is limited to streamflow data as a predictor.     

基于决策树的软件项目估算方法

软件工程领域的一个重要问题是预测软件开发项目的规模、工作量和成本,即软件项目估算问题。基于机器学习的方法在软件项目估算领域具有优势地位,本文提出了基于决策树的聚类分析预测方法,通过对目标项目的目标属性进行正确分类,预测目标属性的取值范围。通过对502个ISBSG v9项目数据集中的项目进行基于C4.5算法的分类预测,正确率达到82.4701%,满足了软件项目估算的指标要求。     

Traffic prediction for dynamic traffic engineering

Traffic engineering with traffic prediction is a promising approach to accommodate time-varying traffic without frequent route changes. In this approach, the routes are decided so as to avoid congestion on the basis of the predicted traffic. However, if the range of variation including temporal traffic changes within the next control interval is not appropriately decided, the route cannot accommodate the shorter-term variation and congestion still occurs. To solve this problem, we propose a prediction procedure to consider the short-term and longer-term future traffic demands. Our method predicts the longer-term traffic variation from the monitored traffic data. We then take account of the short-term traffic variation in order to accommodate prediction uncertainty incurred by temporal traffic changes and prediction errors. We use the standard deviation to estimate the range of short-term fluctuation. Through the simulation using actual traffic traces on a backbone network of Internet2, we show that traffic engineering using the traffic information predicted by our method can set up routes that accommodate traffic variation for several or more hours with efficient load balancing. As a result, we can reduce the required bandwidth by 18.9% using SARIMA with trend component compared with that of the existing traffic engineering methods.     

WSN中基于GM-LSSVM的数据融合方法

针对传统时间序列预测融合算法对于具有非线性、随机性和突发性的数据拟合度不佳的问题,提出了一种基于灰色最小二乘支持向量机(GM-LSSVM)预测的时序数据融合方法.利用少量监测数据对模型进行训练,以灰色回归预测数据作为最小二乘支持向量机的输入数据,并对下一步未知信息进行预测,以达到减少通信开销的目的.实际测量结果表明,该方法所需样本数量较少,预测准确率较高,能有效降低数据传输开销.     

四维飞行轨迹预测方法的研究

通过对空中交通运输管理中目前常用的轨迹预测算法的研究比较和分析,提出了利用遗传算法的从历史数据中进行函数挖掘的思想.针对四维轨迹数据特征的分析和传统的单一函数挖掘的局限性,提出了基于基因表达式编程的频繁函数集挖掘的建模方法.该模型方法通过对历史飞行数据进行遗传算法的操作挖掘出数据集中对应的函数关系集合,用较好的函数模型预测未来航迹.以某一航班雷达数据为训练集做实验,结果表明了应用该方法的准确性和可用性.     

Sequential manifold learning for efficient churn prediction

Nowadays, thanks to the rapid evolvement of information technology, an explosively large amount of information with very high-dimensional features for customers is being accumulated in companies. These companies, in turn, are exerting every effort to develop more efficient churn prediction models for managing customer relationships effectively. In this paper, a novel method is proposed to deal with a high-dimensional large data set for constructing better churn prediction models. The proposed method starts by partitioning a data set into small-sized data subsets, and applies sequential manifold learning to reduce high-dimensional features and give consistent results for combined data subsets. The performance of the constructed churn prediction model using the proposed method is tested using an E-commerce data set by comparing it with other existing methods. The proposed method works better and is much faster for high-dimensional large data sets without the need for retraining the original data set to reduce the dimensions of new test samples.     

知识图谱技术在全国取用水平台的应用

全国取用水平台整合是实现取用水业务“一网统管”的重要途径，但因拟整合的取用水业务系统繁多，信息资源分散，平台整合中存在一数多源、标准不统一、户证点关系不清晰等现实问题，由此尝试提出了利用知识图谱技术应用于平台整合过程，建设一套全国统一的数据库表结构标准，形成一套真实且唯一的户证点关系，构建一套智能且有效的监管数据产品。本文采用自顶向下的知识图谱构建方式，根据各系统的结构化数据，构造模式层中的本体及其相互关系，形成对应的概念模型和规则关系，然后依照此模式从数据中抽取实体及关系，进行数据融合，构造数据层，并及时进行知识更新，完成全国取用水平台知识图谱的构建。最后基于统一规范的数据库表结构标准，依据对取用水户取用水行为监管需求，开展时空尺度的数据信息统计分析，构建六类监管产品，形成取用水管控“一张图”。     

基于分层数据筛选的跨项目缺陷预测方法

跨项目缺陷预测旨在解决传统的项目内缺陷预测的历史数据缺失,新项目初期缺乏训练数据等实际问题。然而,在跨项目缺陷预测中,不同项目之间以及实例之间的数据分布差异降低了其预测性能。针对这一问题,提出了基于分层数据筛选的跨项目缺陷预测方法。该方法将训练数据的筛选过程分为项目层筛选和实例层筛选,从源数据集中选出与目标项目数据分布最接近的候选项目集,在候选项目集中选出与目标项目中实例相似度较高的训练数据集,最后在训练数据集上训练朴素贝叶斯模型。在PROMISE数据集进行实验对比。结果表明,与项目内缺陷预测比较,提出的分层数据筛选方法优于项目内缺陷预测,并且有效降低了训练数据和目标项目数据之间的差异性。