森林火险中长期预测预报研究进展

概述了我国和美国、加拿大的森林火险中长期预测预报的理论和方法。国内研究大多建立在对历史火灾资料与气象资料进行统计分析研究的基础上, 预测结果多是定性的、间断性的。美国和加拿大则依托成熟的全国性森林火险等级系统, 结合气象局的中长期降水和温度预报, 给出各火险指数实时的一周尺度预报及每周1次的16周尺度的季节性预报。我国今后应加强森林火险的中长期预测预报研究, 要由单纯定性分析转向定量化研究, 由一般数理统计模型转向具有明显物理意义的数学物理模型。     

应用性诱剂防治甘蔗螟虫

对甘蔗螟虫的为害特点和防治现状以及性诱剂的特点和应用现状进行了概述。为了有效防治甘蔗螟虫,利用性诱剂进行预测预报,指导适时防治具有重要意义。     

Weather-Centric Rangeland Revegetation Planning

Invasive annual weeds negatively impact ecosystem services and pose a major conservation threat on semiarid rangelands throughout the western United States. Rehabilitation of these rangelands is challenging due to interannual climate and subseasonal weather variability that impacts seed germination, seedling survival and establishment, annual weed dynamics, wildfire frequency, and soil stability. Rehabilitation and restoration outcomes could be improved by adopting a weather-centric approach that uses the full spectrum of available site-specific weather information from historical observations, seasonal climate forecasts, and climate-change projections. Climate data can be used retrospectively to interpret success or failure of past seedings by describing seasonal and longer-term patterns of environmental variability subsequent to planting. A more detailed evaluation of weather impacts on site conditions may yield more flexible adaptive-management strategies for rangeland restoration and rehabilitation, as well as provide estimates of transition probabilities between desirable and undesirable vegetation states. Skillful seasonal climate forecasts could greatly improve the cost efficiency of management treatments by limiting revegetation activities to time periods where forecasts suggest higher probabilities of successful seedling establishment. Climate-change projections are key to the application of current environmental models for development of mitigation and adaptation strategies and for management practices that require a multidecadal planning horizon. Adoption of new weather technology will require collaboration between land managers and revegetation specialists and modifications to the way we currently plan and conduct rangeland rehabilitation and restoration in the Intermountain West.     

Improving cereal yield forecasts in Europe – The impact of weather extremes

The impact of extreme events (such as prolonged droughts, heat waves, cold shocks and frost) is poorly represented by most of the existing yield forecasting systems. Two new model-based approaches that account for the impact of extreme weather events on crop production are presented as a way to improve yield forecasts, both based on the Crop Growth Monitoring System (CGMS) of the European Commission. A first approach includes simple relations – consistent with the degree of complexity of the most generic crop simulators – to explicitly model the impact of these events on leaf development and yield formation. A second approach is a hybrid system which adds selected agro-climatic indicators (accounting for drought and cold/heat stress) to the previous one. The new proposed methods, together with the CGMS-standard approach and a system exclusively based on selected agro-climatic indicators, were evaluated in a comparative fashion for their forecasting reliability. The four systems were assessed for the main micro- and macro-thermal cereal crops grown in highly productive European countries. The workflow included the statistical post-processing of model outputs aggregated at national level with historical series (1995–2013) of official yields, followed by a cross-validation for forecasting events triggered at flowering, maturity and at an intermediate stage. With the system based on agro-climatic indicators, satisfactory performances were limited to microthermal crops grown in Mediterranean environments (i.e. crop production systems mainly driven by rainfall distribution). Compared to CGMS-standard system, the newly proposed approaches increased the forecasting reliability in 94% of the combinations crop × country × forecasting moment. In particular, the explicit simulation of the impact of extreme events explained a large part of the inter-annual variability (up to +44% for spring barley in Poland), while the addition of agro-climatic indicators to the workflow mostly added accuracy to an already satisfactory forecasting system.     

基于LM算法的BP神经网络的电力负荷短期预测

清代书院课艺总集多为连续出版物，或具有连续出版物的刊行初衷。刊期短则一季，多则一年或数年。经费充足与否，会影响刊期。发表周期多为一年至五年，也有十余年的。用稿率以10%~20%居多，偶见“关系稿”。时文的用稿标准是“清真雅正”。题目多为官师所拟。一般全文刊登，也偶有“论点摘编”。多经润色，并附录评点。有的以袖珍本刊行，有的宣称“翻刻必究”，标出定价，附载广告。稿费已在膏火费中预支，优秀作品可被转载。从本质属性和诸多要素来看，书院课艺总集实开今日“大学学报”、“学术集刊”之先河。     

细分外推与模糊预测在近海水质非线性时序处理中的比较分析

近海水质非线性时间序列通常由于采集范围大、时间间隔长带有一定震荡性和模糊性,这使得对其进行分析与预测有一定的难度。本研究中以某近海水质指标磷酸盐(PO3-4-P)、硝酸盐(NO-3-N)、亚硝酸盐(NO-2-N)、铵盐(TNH+4-N)和硅酸盐(Si O2-3-Si)所形成的5种时间序列为例,采用逼近细分模式导出的细分外推法和多参考加权数据的模糊预测法对近海水质时序预测进行了比较分析,并通过图形与误差计算比较了两种方法的异同。结果表明:采用细分外推法预测序列在整体形状上能更好地逼近初始时序,而模糊预测法在整体逼近精度上占有优势。本研究中提出的预测比较方法可为同类问题的预测与模型选取提供参考依据。     

金融时间序列指标判别框架：以特质波动率为例

基于拐点集合判别的TBUD方法主要思路是分析拐点集合间的关系，并在高维空间进行划分，从而搭建判别模型，并将分析框架应用在特质波动率等若干指标上，利用实证数据得到结论。应用TBUD判别框架可以发现，特质波动率等指标无法对拐点集合进行清晰划分，因而并不具有预测能力。     

微分响应在降雨误差修正中的应用

误差修正是实时洪水预报研究和应用中的重要内容,从修正模型降雨输入角度出发,提出了一种基于微分响应的降雨误差修正方法,并通过实例论证了该方法修正降雨量的适用性。该方法通过计算降雨所对应模型的微分响应来修正降雨,从而修正出口断面流量过程。将该修正方法结合新安江模型,对闽江建阳流域的19场历史洪水进行了有效性检验,结果表明:此方法对洪量、洪峰的修正效果明显,且能够显著提高洪水预报的合格率,修正后合格率由63.2%提高到94.7%。     

Application of Attribute Reduct Method in Spatial Load Forecasting

Spatial load forecasting is a process distributing the total forecasted load to all partitioned area, and involving more spatial information and more factor influencing application of the future small area, which need a great deal of memory space and longer operation time. Rough set is new method of data analysis. It need not be provided with any advanced information except data set. But attribute reduct is its main algorithms. Division matrix approach on rough set used to reduce the attribute related to land - use decision in order to remove redundancy attribute and then the rules of small area land - use decision is distilled. The method obtained better effect and enhanced the total load forecasting efficiency.     

基于KPCA-WLSSVM的建筑能耗预测模型

为降低建筑能耗影响因素间复杂相关性对模型性能的影响,建立了一种基于KPCA-WLSSVM的建筑能耗预测模型。利用核主元分析(KPCA)对输入变量进行数据压缩,消除变量之间的相关性,简化模型结构;进一步采用加权最小二乘支持向量机(WLSSVM)方法建立建筑能耗预测模型,同时结合一种新型混沌粒子群-模拟退火混合优化(CPSO-SA)算法对模型参数进行优化,以提高模型的预测性能及泛化能力。通过将KPCA-WLSSVM模型方法应用于某办公建筑能耗的预测中,并与WLSSVM、LSSVM及RBFNN模型相比,实验结果表明,KPCA-WLSSVM模型方法能有效提高建筑能耗预测精度。