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.     

Modeling the Suitability of Potential Wetland Mitigation Sites with a Geographic Information System

Wetland mitigation is frequently required to compensate for unavoidable impacts to wetlands. Site conditions and landscape context are critical factors influencing the functions that created wetlands perform. We developed a spatial model and used a geographic information system (GIS) to identify suitable locations for wetland mitigation sites. The model used six variables to characterize site conditions: hydrology, soils, historic condition, vegetation cover, adjacent vegetation, and land use. For each variable, a set of suitability scores was developed that indicated the wetland establishment potential for different variable states. Composite suitability scores for individual points on the landscape were determined from the weighted geometric mean of suitability scores for each variable at each point. These composite scores were grouped into five classes and mapped as a wetland mitigation suitability surface with a GIS. Sites with high suitability scores were further evaluated using information on the feasibility of site modification and project cost. This modeling approach could be adapted by planners for use in identifying the suitability of locations as wetland mitigation sites at any site or region.     

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.     

Geographic Analysis of Forest Health Indicators Using Spatial Scan Statistics

Geographically explicit analysis tools are needed to assess forest health indicators that are measured over large regions. Spatial scan statistics can be used to detect spatial or spatiotemporal clusters of forests representing hotspots of extreme indicator values. This paper demonstrates the approach through analyses of forest fragmentation indicators in the southeastern United States and insect and pathogen indicators in the Pacific Northwest United States. The scan statistic detected four spatial clusters of fragmented forest including a hotspot in the Piedmont and Coastal Plain region. Three recurring clusters of insect and pathogen occurrence were found in the Pacific Northwest. Spatial scan statistics are a powerful new tool that can be used to identify potential forest health problems.     

Spatial Pattern of Ecosystem Function and Ecosystem Conservation

The spatial pattern of ecosystem function can affect ecosystem conservation. Ecosystem functions are often heterogeneous spatially due to physical and biological factors. We can influence ecosystem functions by changing the spatial patterns of the physical and biological elements of an ecosystem and regulating their combinations. The variation–position effect highlights a phenomenon resulting from the spatial pattern of ecosystem function. The effect shows that the identical variation of a factor may produce different effects on the overall situation when this variation occurs in a different spatial position. In a watershed of the Yangtze River, water retention is a primary ecosystem function. The variation–position effect for water retention capacity occurs in the watershed because of the spatial heterogeneity in vegetation, soil, and slope. The change of vegetation that occurs in a complex can affect the overall situation of water retention, and the effect can be different due to the change occurring in the position holding different vegetation-soil-slope complex. To improve the ecosystem in the watershed and to meet the social needs for the ecosystem function of water retention, a strategy called ecosystem function and spatial pattern-based forest extension was proposed to conserve forests. The implementation of the strategy enables the watershed to attain the maximum effective increase in water retention capacity.     

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。     

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.