The hybrid model of empirical wavelet transform and relevance vector regression for monthly wind speed prediction

ABSTRACT

In order to improve the prediction ability for the monthly wind speed of RVR, the hybrid model of empirical wavelet transform and relevance vector regression (EWT-RVR) is proposed for monthly wind speed prediction in this study. Compared with empirical mode decomposition (EMD), empirical wavelet transform (EWT) can obtain a more consistent decomposition and have a mathematical theory. In order to testify the superiority of EWT-RVR, several traditional RVR models are used to compare with the proposed EWT-RVR method under the situation of the same embedding dimensions. The experimental results show that the proposed EWT-RVR method has a better prediction ability for monthly wind speed than RVR. It can be concluded that the proposed EWT-RVR method for monthly wind speed is effective.     

3S技术在财产保险防洪救灾中的应用

分析了保险业防灾减损的研究现状,阐述了3S的技术特点及其应用领域。和如何将3S技术应用于财产保险防洪救灾领域,建立财产保险防洪救灾决策支持系统。介绍了系统的整体构架及其功能特点,该系统可与保险公司业务系统紧密结合,显示直观。同时包括保险分析、灾害分析和专题图显示等多项功能。实现了多源数据的无缝集成。因而能从理论方法和关键技术上指导保险公司的防灾减损工作。切实提高保险公司的经济效益。以深圳市为示范区,该系统在应用中取得了良好效果。     

Predicting mass rapid transit noise levels on an elevated station

This study developed a noise prediction model for elevated mass rapid transit (MRT) platforms. Relevant physical and operational parameters (e.g. cruise speed, acceleration and deceleration rates for trains, building fa?ade setbacks and so on) were collected from the Bangkok mass transit system (BTS), the first elevated MRT system operated in Bangkok, Thailand. The equivalent continuous sound pressure levels (L(Aeq)) were collected from both sides of the MRT stations at the center of each platform. The relevant parameters were collected on both platforms and ground level, on both sides of MRT stations. These parameters were statistically tested to determine their correlation with MRT noise. The final model was built from highly correlated parameters using multiple regression analysis with a stepwise regression technique. Statistical evaluation showed a high degree of goodness-of-fit test for the model to the observed data. Therefore, it can be efficiently used for the projection of MRT noise in the affected areas.     

Evaluating Nonpoint Pollution Policy Using a Tightly Coupled Spatial Decision Support System

Policy makers often must rely on the cumulative impact of independent actions taken by local landowners to achieve environmental goals. The connection between policy, regulation, and local action, however, is often not well understood and, thus, the impact of proposed policies may be difficult to predict. In this study we evaluate the effectiveness of alternative policy scenarios for agricultural set aside programs (e.g., the conservation reserve program administered by the United States Department of Agriculture) in reducing nonpoint pollution. Two alternative policy scenarios are developed and analyzed; one based on the erodibility index (detachment), the other sediment yield (transport). An estimate of the cumulative impact of associated land use change on nonpoint pollution is made using the AGNPS distributed parameter watershed model. This work is completed within the Cypress Creek watershed in southern Illinois. An analysis of the resulting data suggests that the most efficacious regulatory strategy for achieving nonpoint water pollution goals depends, in part, on place-specific land use patterns. This conclusion provides a solid argument for place-based regulatory strategies.     

安全投入与事故直接经济损失的问题研究

通过对安全投入及事故直接经济损失的分析研究,利用最小二乘法对安全投入比例(安全投入占GDP的比例)与事故直接经济损失之间的关系进行了回归分析,确定了其对应的函数关系.结果表明,安全投入比例与事故直接经济损失之间呈较明显的负指数关系.探讨了安全投入存在的主要问题,提出了几点关于加强安全投入的建议及措施.     

基于优势隐含周期分析的气象灾害预报

建立了优势因子和优势隐含周期的概念,并提出了多因子逐步回归周期分析的新方法。该方法既能增长预报期,又能充分利用物理因子的重要信息,同时在计算过程中还能挑选出起显著作用的优势因子和优势隐含周期,对预报气象要素的变化趋势效果较好,有一定的实用价值,是气象灾害预报的一条比较有效的途径。     

我国农业自然灾害与农业政策性保险

在详细分析了我国当前主要农业自然灾害情况的基础上,探讨了通过建立农业政策性保险来应对自然灾害风险的必要性以及我国农业保险目前面临的问题,提出了加大政府支持力度、加快农业保险立法、提高保险意识、加强保险人才培养等方面的政策建议。     

Water Use by Thermoelectric Power Plants in the United States1

Abstract: Thermoelectric power generation is responsible for the largest annual volume of water withdrawals in the United States although it is only a distant third after irrigation and industrial sectors in consumptive use. The substantial water withdrawals by thermoelectric power plants can have significant impacts on local surface and ground water sources, especially in arid regions. However, there are few studies of the determinants of water use in thermoelectric generation. Analysis of thermoelectric water use data in existing steam thermoelectric power plants shows that there is wide variability in unitary thermoelectric water use (in cubic decimeters per 1 kWh) within and among different types of cooling systems. Multiple‐regression models of unit thermoelectric water use were developed to identify significant determinants of unit thermoelectric water use. The high variability of unit usage rates indicates that there is a significant potential for water conservation in existing thermoelectric power plants.     

THE EFFECT OF DATA INDEPENDENCE IN MODEL CALIBRATION AND MODEL TESTING1

ABSTRACT: With the increased use of models in hydrologic design, there is an immediate need for a comprehensive comparison of hydrologic models, especially those intended for use at ungaged locations (i.e., where measured data are either not available or inadequate for model calibration). But some past comparisons of hydrologic models have used the same data base for both calibration and testing of the different models or implied that the results of model calibration are indicative of the accuracy at ungaged locations. This practice was examined using both the regression equation approach to peak discharge estimation and a unit hydrograph model that was intended for use in urban areas. The results suggested that the lack of data independence in the calibration and testing of regression equations may lead to both biased results and misleading statements about prediction accuracy. Additionally, although split-sample testing is recognized as desirable, the split-samples should be selected using a systematic-random sampling scheme, rather than random sampling, because random sampling with small samples may lead to a testing sample that is not representative of the population. A systematic-random sampling technique should lead to more valid conclusions about model reliability. For models like a unit hydrograph model, which are more complex and for which calibration is a more involved process, data independence is not as critical because the data fitting error variation is not as dominant as the error variation due to the calibration process and the inability of the model structure to conform with data variability.