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.     

吐鲁番地区粮食生产发展问题初探

吐鲁番盆地的特殊气候条件，为经济作提供了良好的生长环境，而粮食生产受气候条件和经济作物快速增长的双重制约，发展非常困难，本文分析了吐鲁番地区粮食生产增长态热及制约因素，并应用多种模型对未来粮食变化趋势作了定量和定性的，在此基础上提出了相应的对策。     

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.     

江苏地区地下水位固体潮加卸载响应比的时空演变特征及预测意义

根据加卸载响应比理论,通过对1991～1996年江苏省部分井孔观测资料固体潮加卸载响应比的计算发现,在中强地震前一年左右的时间震中区附近的井孔出现了响应比高值异常。通过空间图像扫描可以看出:在苍山地震前一年左右,徐州、宿迁地区出现了加卸载响应比的高值集中区;在南黄海地震前一年左右,在镇江、苏州地区出现了高值集中区,之后高值集中区有自西向东迁移即有向震中区迁移的迹象。     

火灾后钢筋混凝土损伤程度的灰关联分析

由于火灾对钢筋混凝土结构的影响存在不确定性,使得人们不能准确地确定火灾后钢筋混凝土结构的损伤程度,给评估和加固修复带来很大困难。本文运用灰色系统理论中灰关联分析数学模型定量分析了高温下钢筋混凝土梁的3种影响因素,并确定了各因素间的主次关系,为高温后钢筋混凝土结构的安全评估及修复加固提供了理论依据。     

Conservation strategies for effective land management of protected areas using an erosion prediction information system (EPIS)

This research demonstrates the predictive modeling capabilities of a geographic information system (GIS)-based soil erosion potential model to assess the effects of implementing land use change within a tropical watershed. The Revised Universal Soil Loss Equation (RUSLE) was integrated with a GIS to produce an Erosion Prediction Information System (EPIS) and modified to reflect conditions found in the mountainous tropics. Research was conducted in the Zenzontia subcatchment of the Río Ayuquíla, located within the Sierra de Manantlán Biosphere Reserve (SMBR), México. Expanding agricultural activities within this area will accentuate the already high rate of soil erosion and resultant sediment loading occurring in the Río Ayuquíla. Two land-use change scenarios are modeled with the EPIS: (1) implementation of soil conservation practices in erosion prone locations; and (2) selection of sites for agricultural expansion which minimize potential soil loss. Confronted with limited financial resources and the necessity for expedient action, managers of the SMBR can draw upon the predictive capacity of the EPIS to facilitate rapid and informed land-use planning decisions.     

人工神经网络方法在资源与环境预测方面的应用

用人工神经网络方法对不同水域、不同环境因子之间非线性和不确定性的复杂关系进行学习训练并预测检验。结果表明：人工神经网络方法在模拟和预测方面 优于传统的统计回归模型,在资源与环境方面的应用是可行的。具有较强的模拟预测能力。与传统的回归模型相比,人工神经网络方法不要求监测数据具有很强的规律性,就可用后的网络模型对其进行预报,燕且预测相对误差均比回归模型预测相对误差要小,具有一定的实用性。两个实例的应用     

贵港市土地利用动态变化分析及用地预测

以城市为中心的区域土地是土地资源中资产效益最高的一部分，是人类利用土地影响最为深刻的土地类型。在分析土地利用现状变更调查数据的基础上，应用各类土地利用动态变化模型对贵港市的土地利用动态变化进行分析，并运用灰色系统预测模型对该区域各地类的土地利用时空演变进行了预测，最后提出相应的优化土地利用结构的建议。     

组合优化的能源消费量预测模型

针对非等间距灰色系统预测中存在误差较大的问题，结合序列本身的特点，利用世界能源消费的历史数据，采用3种灰色预测模型与神经网络进行组合优化，建立了灰色神经网络的能源消费量组合预测模型。实证分析结果表明，提高了模型的拟合和预测精度，拓宽了应用范围。该模型可对能源的消费趋势进行预测，为科学分析能源结构提供依据。