Determination to Weighting Coefficient of Combination Forecast Based on Rough Set Theory

The problem of determination to weighting coefficient is a key and difficulty for combination forecast. A method of determining weighting coefficient based on rough set theory is showed in this paper. Determining weighting coefficient is translated into estimating significance of attributes among rough set. A relation data model about combination forecast is established. Knowledge systems are built through making attribute value into eigenvalue. Under data moving, the weighting coefficients of a combination forecast model are computed by analyzing the dependence and significance of forecasting method for the predicted object. The proposed approach overcomes the subjectivity of traditional determination to weighting coefficient, avoids computing linear or nonlinear extremum problem and makes combination forecast more objective. The validity of the proposed approach is verified with a case.     

Alternative Labor Market Closures in a Regional Model

Five versions of a regional economic forecasting and simulation model are implemented to evaluate the forecasting accuracy and significance for impact analysis of alternative regional labor market closures. The five versions correspond to the following specifications: downward-sloped labor demand and upward-sloped labor supply, vertical labor demand and upward-sloped labor supply, an input-output version, and two general equilibrium configurations of labor demand and supply. It is found that the estimated impacts of an exogenous employment stimulus differ greatly across the model versions. Also, post-sample forecasts for 1981-1988 are run for the fifty states plus Washington D.C. with each model version to test their relative forecast accuracy. The forecast comparison shows that the general equilibrium version that specifies inelastic supply is inferior to the other versions for short-term forecasts of wage rates and long-term employment forecasts. For both short- and long-run population forecasts, the versions with completely immobile labor are more accurate than those with completely mobile labor. However, versions that specify an upward-sloped labor supply (partial labor supply adjustment) are the most accurate.     

日光温室小气候预报技术研究

为了提高设施农业气象服务水平,促进中国设施农业发展,通过对日光温室内外气象条件对比观测,分别采用BP神经网络、逐步回归和能量平衡原理构建了温室内气温预报模型,探讨能适用于业务服务的小气候预报技术方法。结果表明,基于BP神经网络的预报模型虽预报精度高,由于种植作物生长特性差异较大,缺乏服务的广适性;通过能量平衡原理构建的预报模型机理性强,但相关参数难以获得,预报精度差,服务时效短;采用逐步回归方法构建的温室温度预报模型较前两者具有比较优势,且预报时效可以为未来的1~7天,比较适合于目前气象部门开展设施农业气象服务。     

基于产地型冷链物流市场的冷库布局规划

由于西北地区物流行业滞后,农产品冷链运输设施匮乏,冷链物流制约着农产品产业在该地区的持续发展。以西北地区为例进行具体分析,首先运用灰色预测模型对未来西北地区冷链物流产品产量进行预测,并利用层次分析(AHP)综合评估法确认冷链物流中转节点冷库位置;在此基础上构建线性规划模型,并在基于冷库建设成本和运输成本最小化的约束条件下,运用LINGO软件求解,得到西北地区冷库建设布局最优解。最后通过上述分析,提出西北地区未来冷链物流发展方向和建议,即区域发展和依托发展更有益于我国西北地区冷链发展。     

基于气候适宜指数的湖南早稻产量动态预报

早稻生长发育和产量形成过程与气象条件密切相关,开展早稻产量动态预报对湖南农业生产和粮食安全具有重要意义。从湖南早稻生长发育的上限温度、最适温度、下限温度、需水量、需光特性等生物学特性出发,建立湖南早稻气候适宜度模型,采用权重系数的方法构建湖南早稻气候适宜指数。选取15个代表站点,统计分析1961—2009年不同时段湖南早稻气候适宜指数与产量丰歉值的关系,建立了基于气候适宜指数的湖南早稻产量动态预报模型,并从模型预测值与实测值增减趋势一致的样本百分率、预报准确率等方面进行回代检验。利用2010—2012年资料进行外推检验。检验结果表明,建立的预报时间为4月30日、5月20日和6月20日的基于气候适宜指数的湖南早稻产量预报模型预报值与实测值增减趋势一致的样本百分率为64%~73%,预报准确率平均为94.7%~96.3%。研究结果表明,建立的基于气候适宜指数的早稻产量动态预报模型,能够满足湖南早稻产量预报业务服务的需要。     

Cost forecast model for power engineering based on based on back propagation neural networks

A novel power engineering cost forecast model was proposed by combining feature extraction and small sample learning. The initial data was preprocessed with principal component analysis to remove the correlation among the original indexes and get the potential independent indexes. The new indexes acted as the input set to build a new forecast model based on least squares support vector machines. The results of this model were compared with the forecast results getting from artificial neural network. By comparing the forecast results with different principal components number, the optimal number was determined to achieve the desired forecast effect. The prediction results indicate that the method can extract the feature of initial data effectively and is good at small sample learning . The expected forecasting results can be reached.     

基于数值预报产品的地表温度预报方法研究

为了开展地表温度预报业务,提高逐日地表温度预报准确率,利用2007—2012年的ECMWF和T213数值预报产品资料及抚顺市的逐日地表温度资料,采用逐步回归分析方法和BP神经网络模型分别构建抚顺市地表温度预报模型,并对模型的精度进行检验。结果表明,地表温度与ECMWF的高度场、海平面气压场、温度场和T213的散度场、高度场、海平面气压场、地面气压场、海平面K指数、水汽通量、相对湿度、温度场、地面气温和场涡度场均呈显著相关。对预报模型进行精度检验显示,地表平均温度和地表最低温度的预报效果较好,≤3℃预报准确率均达到79%以上。2种模型对比显示,BP神经网络预报模型总体上优于逐步回归预报模型;逐步回归预报模型较BP神经网络预报模型稳定。     

山西省冬小麦主要病虫害气象等级预报模型

气象条件是影响农作物病虫害的主要因子,且与作物的发育期密切相关,特别是在特殊年份,如果模型所筛选的因子中较常年有明显的改变,预测结果与实际结果相差较大,因此各预测模型都具有一定的局限性。为提高数理统计预报的实用性,需要采用多年的历史资料,同时在实际工作中应采用尽量多的方法进行预测,以最大限度地获得较高的预测准确率。本文以山西冬小麦的主要病虫害（白粉病、条锈病、麦蜘蛛）为研究对象,研究其发生流行的气象条件预报指标,并建立山西省冬小麦主要病虫害发生的气象条件预测模型,进行山西省冬小麦主要病虫害发生的气象条件预测。通过历史资料进行回测验证,历史拟合准确率达到90%以上,说明应用该方法所建模型在一般年份可以进行冬小麦条锈病、白粉病、麦蜘蛛的预测。     

Application of the Forecasting Model of Exponential Smoothing to the Forecast of the Coal Wastage in Chongqing

The forecasting model of exponential smoothing is a valid method in forecasting and decision-making.Based on the practical datas and according to the characteristic(linearity trend) of the fact coal wastage in Chongqing(recently),the forecasting model of twice exponential smoothing model is set up and the model is applied to forecast the caol wastage in Chongqing.The forecast results show that the errors between the forecast values and the fact values error little and the model may be well applied on the forecast of the coal wastage in Chongqing.It also shows that the decision-making can based on the result well.     

基于分数阶灰色模型的生鲜电商产品销量预测研究

新型冠状病毒的爆发,使供应链受到了不良影响,蔬菜水果等生鲜商品线下批发零售受阻,生鲜电商却迎来了新一轮的发展.从生鲜消费者角度出发,通过构建分数阶灰色预测模型对生鲜产品的需求量进行预测,并引入甘福园生鲜电商销售数据进行验证,得到柠檬、车厘子、苹果及火龙果4种水果拟合值的平均绝对百分比误差(MAPE)分别为8.61％、7.15％、7.16％、6.81％,其值均小于灰色预测模型和一次指数平滑法,且由该模型得到预测值的平均绝对百分比误差均小于10％,结果表明该模型适用于生鲜电商产品销量预测.     

农业气象服务满意度测评方法研究

为了深入了解农户对农业气象服务的需求,有效地避免和降低气象灾害及次生灾害带来的危害和损失,真正发挥农业气象服务的作用,结合研究当地农业气象服务的特点,构建农业气象服务用户满意度测度模型(CSIAS),通过数学方法计算出农业气象服务用户对农业气象服务的满意度指数,分析用户对农业气象服务的需求结构。应用满意度测度模型,结合2015 年武威市农业气象服务满意度调查问卷进行实例分析。结果显示,用户对当地农业气象服务满意度比较高,将来对气象服务产品继续保持较高的关注度,但是对于天气预报准确率,尤其是灾害性天气预报的准确率满意度较低,这就要求气象部门在提高天气预报准确率这方面还需努力。此次农业气象服务满意度评价,使气象部门能够合理地确定服务产品重点,找出为农服务产品中的不足,改进服务内容,为今后农业气象服务工作的改进奠定结实的基础。     

桂花花期气象预报研究

为了更准确地提供桂花开花期的预报,利用桂林市近15年的始花期观测资料,普查分析气象原因,挑选气象因子,用统计分析方法,建立预报模式。结果表明,温度、湿度、日照、降水对桂花开花期有明显影响;影响桂花花期的原因比较复杂,在实际进行花期预报时,根据时间提前的长短,又可以分为长期预报和短期预报,长期预报又为气候预测,只是花期偏早或偏迟的趋势预测,短期预报则是临近开花前1周的预报。长期预报模式和短期预报模式在2013年的业务应用中取得了较好的效果。     

Regional Indexes of Leading Indicators: An Evaluation Of Forecasting Performance

ABSTRACT Indexes of leading indicators are used to forecast short-run changes in economic activity in many states and metropolitan areas. This article examines the forecasting performance of a sample of such regional indexes over several business cycles. Although these indexes vary across regions, the results show that they do provide forecasts of recessions and recoveries and also exhibit some potential for forecasting quantitative changes in regional employment.     

黑龙江省三种水稻热量指数预测方法的对比研究

旨在选出一套适合黑龙江不同区域水稻低温冷害预测方法,为相关部门制定粮食生产和调整农作物种植结构提供科学依据。选择黑龙江省11个水稻农气观测站为研究对象,利用1971—2016年的气温资料、74类大气环流资料、水稻发育期数据,将黑龙江省划分为东、西、南3个区域,分别建立逐步回归预测模型、GM(1,1)灰色预测模型和均生函数预测模型,预测黑龙江水稻生育期总热量指数并进行对比分析。结果表明：建立的3种预测模型通过了残差检验,1971—2010年拟合平均准确率均在95%以上,结果差异不大;2011—2016年的试报准确率为85%~99%,其中GM(1,1)灰色预测模型准确率(97%~99%)高于逐步回归预测模型(91%~97%)和均生函数预测模型(85%~95%)。通过3种预测方法对比结果显示,GM(1,1)灰色预测模型模拟效果最好。     

枯黄期草原火灾精细化风险预测研究

为增强对草原火灾风险评估和应急管理能力,利用“智能网格”精细化气象要素预报数据、DGI指数、NDSI指数、DEM数据和社会经济数据,依据自然灾害风险理论,构建枯黄期草原火灾精细化风险预测模型。使用ANUSPLIN插值软件把所有因子插值为相同空间尺度数据,GIS栅格计算未来草原火灾风险预报,结果分为高风险、次高风险、中风险、次低风险、低风险区域,并统计出面积及人口等信息,开发出具有业务应用能力的草原火灾风险预测产品。随着气象行业供给侧改革深入开展,精细化气象服务水平不断提高,专业气象应用多样化发展,本研究为新型火险预测产品开发思路提供理论借鉴。     

上海市水产品消费需求的双对数模型分析

为了研究上海市水产品消费需求的影响因素以及不同因素贡献度的大小,以上海市1999—2010年城市居民家庭人均水产品消费量、城市化水平、水产品价格指数、替代品价格指数、居民消费价格指数、居民家庭人均可支配收入等相关数据作为研究对象,采用Eviews软件对上海市水产品消费需求进行回归分析。建立了包括城市居民家庭人均可支配收入、居民消费价格指数、总人口数这些影响因素在内的上海市水产品消费需求模型。另外,通过建立关于城市居民家庭人均可支配收入、居民价格指数、总人口数的模型,对这些影响因素进行预测,从而可以对上海市水产品消费进行预测。最终认为,要增加上海市水产品的消费需求,必须从提高家庭人均可支配收入、控制居民消费价格指数、发展水产品深加工业、加强物流体系建设以及加强水产品市场质量管理等方面入手,推动上海水产业持续稳定的发展。     

安徽省油菜菌核病气象等级预报方法研究

为满足现代农业气象业务服务中病虫害发生发展气象等级预报需求,利用安徽省江淮、沿江和皖南3个区域代表站1991—2010年油菜菌核病观测数据和气象资料,通过相关分析,确定影响菌核病发生发展的主要影响时段和气象因子,再通过归一化处理、加权等方法得到影响油菜菌核病发生发展的综合气象条件指数。以综合气象条件指数为自变量,以油菜菌核病加权平均病株率为因变量,采用曲线回归方法,建立不同区域代表站油菜菌核病发生发展气象等级预报模型。模型拟合结果表明：江淮、沿江和皖南地区拟合准确率分别为80%、70%和75%;利用模型对2011—2013年发生情况进行预测,结果显示：江淮地区代表站全部正确,沿江和皖南地区代表站均为2年正确,一年与实际发生情况误差一个等级。建立的模型基本上能满足油菜菌核病气象等级预报业务服务的需求。     

Pollen-based predictive modelling of wine production: application to an arid region

A wine forecast model for one of the most arid wine regions of the Europe—Alentejo was improved and tested for the period 1998–2014. During this period, Alentejo region had strong upward trends in wine production associated to the increase of vineyard area. The forecast model was supported on a hierarchical analysis, including the determination of the potential production at flowering by quantifying airborne pollen concentration, followed by a climate based evaluation of the possible impact of fruit-set conditions in the limitation of production. Through the monitoring of airborne pollen flows it is possible to define an accurate main pollen season and determine the regional pollen index that will be used as independent variable in the regional forecast model. The time trend, which was initially removed from data, was then added back to obtain the forecast. Stepwise regression and cross-validation were employed during the period 1998–2014 for calibration of the model used for predicting annual wine production. The developed model explained about 86% of wine variance over the years with absolute average error of 6% for the cross validation and 87% of cases had differences between actual and forecasted wine production below 10%. The reliability and early-indication ability of the proposed forecast model justify their use to respond to a number of government agencies and wine industry concerns and activities.     

Artificial Neural Network Fault Diagnosis System Based on Rough Set Theory

On the basis of fault diagnosis neural network model, in this paper, knowledge representation system of rough set theory is taken as a major tool to delaminate the complex neural network and in which unnecessary properties are eliminated. This method overcomes some shortcomings, such as network scale is too large and the rate of classification is slow. The good effect that reduces the matching quantity of pattern search in classification course is gotten. The structure and algorithm of layered-mining neural network model based on rough set theory are also given. The example shows that this system has higher reference value in practical application.     

Application of Combination Grey Model in Power Load Forecasting

Based on the gray forecast theory, this paper studies the principle and deficiency in power load forecasting by the basic grey model and other improved models, and introduces a new method -the combination grey model to forecast the long-medium power load. Based on an example, the basic grey model, other improved models and combination grey model are used to forecast power load and results of all models are analyzed and compared. The calculation results show that forecasting power load by grey theory is credible and simple. For this type of complex problems such as forecasting the long-medium power load, the combination grey model is specially useful because of it's high precision and facility. The method can be used as one of the tools of forecasting the long-medium power load.