Short-term wind speed forecasting based on random forest model combining ensemble empirical mode decomposition and improved harmony search algorithm

ABSTRACT

Wind speed forecasting plays an important role in power grid dispatching management. This article proposes a short-term wind speed forecasting method based on random forest model combining ensemble empirical modal decomposition and improved harmony search algorithm. First, the initial wind speed data set is decomposed into several ensemble empirical mode functions by EEMD, then feature extraction of each sub-modal IMF is performed using fast Fourier transform to solve the cycle of each sub-modal IMF. Next, combining the high-performance parameter optimization ability of the improved harmony search algorithm, two optimal parameters of random forest model, number of decision trees, and number of split features are determined. Finally, the random forest model is used to forecast the processing results of each submodal IMF. The proposed model is applied to the simulation analysis of historical wind data of Chaoyang District, Liaoning Province from April 27, 2015 to May 22, 2015. To illustrate the suitability and superiority of the EEMD-RF-IHS model, three types of models are used for comparison: single models including ANN, SVM, RF; EMD combination models including EMD-ANN, EMD-SVM, EMD-RF; EEMD combination models including EEMD-ANN, EEMD-SVM, EEMD-RF. The analysis results of evaluation indicators show that the proposed model can effectively forecast short-term wind data with high stability and precision, providing a reference for forecasting application in other industry fields.     

Climate dynamics and extreme precipitation and flood events in Central Europe

Past changes and possible future variations in the nature of extreme precipitation and flood events in Central Europe and the Alpine region are examined from a physical standpoint. An overview is given of the following key contributory physical processes: (1) the variability of the large-scale atmospheric flow and the associated changes of the North-Atlantic storm track; (2) the feedback process between climate warming and the water cycle, and in particular the potential for more frequent heavy precipitation events; and (3) the catchment-scale hydrological processes associated with variations in major river flooding events and that are related to land-use changes, river training measures, and shifts in the proportion of rain to snowfall. In this context an account is provided of the possible future forecasting and warning methodologies based upon high-resolution weather prediction and runoff models. Also consideration is given to the detectability of past (future) changes in observed (modeled) extreme events. It is shown that their rarity and natural fluctuation largely impedes a detection of systematic variations. These effects restrict trend analysis of such events to return periods of below a few months. An illustration using daily precipitation from the Swiss Alps does yield evidence for pronounced trends of intense precipitation events (return period 30 days), while trends of stronger event classes are not detectable (but nevertheless can not be excluded). The small detection probability for extreme events limits possible mitigation of future damage costs through an abatement of climate change alone, and points to the desirability of developing improved early forecasting/warning systems as an additional no-regret strategy. This revised version was published online in July 2006 with corrections to the Cover Date.     

Specific features of seedage in Norway spruce (Picea abies karst.) in the northern Moscow region

Climatic characteristics of the northern Moscow region and the periodicity of seedage in Norway spruce were analyzed retrospectively. The relationship between the intensity of seedage and weather conditions in the period before seedage was revealed.     

Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust.     

粮食产量预测理论、方法与应用Ⅰ.科技进步增产理论、模型及其应用

粮食增产趋势及增产原因是国家制定宏观农业政策和措施的依据。科技进步增产理论是指：气候是波动的,科技是持续进步的,它是粮食多年持续增产的主要驱动力;科技进步增产预测模型是多年平均单产移动的回归方程。全国和东北三省粮食增产潜力案例分析结果表明：科技进步单产加速时间最早的是辽宁省,最晚的是黑龙江省;与全国相比,吉林省和辽宁省科技进步贡献率高于全国平均水平,黑龙江省低于全国平均水平,吉林省最高。本文初步得出以下结论：科技进步增产理论科学、模型实用、预测结果准确。     

G20峰会空气质量保障机制的构建

为进一步做好G20峰会空气质量保障工作,在大气污染联防联控机制的基础上,建立G20峰会空气质量保障机制,形成专项工作小组,按照空气质量保障组织框架运行,为精准保障提供空气质量预报、污染态势特征及评估、措施执行情况及效果、污染溯源等管控技术支撑。2016年9月,G20峰会期间杭州市空气质量总体优良,8月24日至9月7日G20峰会前后杭州市PM_(2.5)、PM_(10)、SO_2、NO_2、CO、O_3等污染物浓度同比2015年分别下降40%、40%、37%、56%、22%、7%,在该机制的推动下空气质量保障工作圆满完成。     

基于EFDC和WASP模型的突发水污染事故影响的预测预警

近年来，我国突发水污染事故频发。长江作为我国最重要的水源地，其水质安全受到沿江众多化工企业可能突发污染事故的威胁。因此，开展南京化工园突发水污染事故影响的预测预警研究意义重大。基于EFDC和WASP模型，建立了南京化工园突发水污染事故影响的预测模型，并以龙翔甲苯罐区泄漏为例进行了事故情景模拟。研究表明：所建的预测模型能够对南京化工园突发水污染事故后污染物的水环境行为进行快速的模拟和预测，并且能够在地理信息系统的图层上以动画的形式实时展示事故影响的范围和程度，可以为南京化工园突发水污染事故的预警应急提供决策依据；基于EFDC构建的二维水动力模型能够较为准确地反映研究区的水动力情况；事故情景中排放的甲苯在研究江段能够较快地稀释扩散，挥发作用对甲苯的衰减过程影响显著，平水期比枯水期更有利于水体中甲苯的衰减     

金竹山土朱煤矿开采地表沉降规律与灰色预测模型研究

针对金竹山矿业公司土朱煤矿煤层赋存条件,依据采动理论的裂缝垂直分带模型,分析地表沉降和塌陷的机理;提出在采煤活动阶段应进行地表实际位移观测,经数据处理后得到地面沉降曲线,以确保地面人类活动的安全;在采煤活动后阶段则实施灰色预测地面沉降,即通过采煤活动阶段的地表实际观测数据为历史原始数据序列,建立灰色Logistic模型;并对采煤活动后阶段的地面沉降进行预测。精度检验表明:灰色Logistic模型预测精度高,利用该模型预测地面沉降可减少地面沉降监测经费和实时提供预警信息,以确保开采区域内人们生命财产安全。     

经度、纬度及时差设置对AERMOD预测结果的影响

为了验证经度、纬度和时差参数对AERMOD模式预测结果是否有影响,假设在某气象条件下存在一个点源,利用AERMOD模式计算该点源排放的污染物的浓度分布.保持气象条件和污染源参数不变,仅改变点源所在地的经度、纬度或时差,重新利用AERMOD模式进行了预测,比较预测结果的差异.结果表明,经度、纬度和时差参数对白天时段行星边界层参数的计算值有影响,受影响的参数包括地表热通量、摩擦速度、Monin-Obukhov长度、对流速度尺度、温度尺度和混合高度.经度、纬度和时差的不同取值对AERMOD计算出的污染物小时平均浓度最大值和浓度分布影响明显.根据AERMOD公式的描述和能量变化得出,经度、纬度和时差的改变引起了项目所在地白天太阳高度角的变化,进而改变了地表热通量.地表热通量的改变,造成了行星边界层参数的变化,最终影响了浓度分布的计算值.     

陕西省的地震危险趋势估计

本文应用计算某一地震带(区)上地震复发周期的公式;T_m=m×10~(bm-a)以及灰色系统理论的动态模型对陕西地区进行了地震危险趋势估计。