水温及其预报方法初探

本着重利用Matlab讨论了水温与气温之间的相关变化关系和利用气温预报水温的初步方法。结合武汉市气象局在武汉市东西湖区养殖场气象观测站1982年至1989年的连续观测资料给出了水温与气温之间的相关系数以及水温预报方程，并讨论了误差及消除误差的途径。     

一种基于季节指数的灰色马尔科夫气温预测模型

提出一种根据气温历史数据的年际周期性和季节性变化规律建立的基于季节指数的灰色-马尔科夫气温预测模型.模型将纵向与横向分析相结合方法运用到气温预报之中,通过季节指数修正气温的横向季节性变化,再用灰色模型进行预测,最后通过马尔科夫进行误差修正.实例运用中,对广州市的2000年月平均气温进行预测,在与历史数据的对比中表明,模型预测结果较为准确,可靠性较好.并讨论说明该模型也可推广到其他具有周期特征的非平稳时间序列的预测中,并大大提高预测精度.     

基于回归BP神经网络误差分级迭代法的逐时气温预测方法研究

气温预测是天气预测中的一项主要内容,由于气温的影响因素多而复杂,要想达到精细化预测目的,仍是十分复杂的科学难题.当前学术界的一般方法是假设数学模型对温度物理过程进行研究,建立了BP神经网络模型、温度与相对湿度之间的回归模型,最终在回归模型的基础上通过改进的BP神经网络建模,即利用BP神经网络误差分级迭代法建模,通过历史温度进行逐时气温预测,全样本误差达到0.617℃.     

中国福利彩票销售额影响因素分析与基于残差主成分分析的预测

本文应用多元回归分析的方法讨论了中国各省份福利彩票销售额与国民经济、就业人员和职工工资、财政、固定资产投资、人民生活这五个大类中的22个预测变量之间的相关关系,找出了对彩票销售额有显著性影响的变量,此外我们发现,不同年份的数据所得到的残差存在强相关性,本文利用主成分分析的观点提取出了不同年份残差中的公共信息,最后得出彩票销售额的预测方程,利用上述方程对2007年各省份的彩票销售额进行预测,并与真实的销售额数据进行对比,获得了较小的平均相对预报误差.这说明本文所建立的模型确实可以对中国的福利彩票销售额进行有效的预测.     

车载跟瞄设备低轨卫星预测方法研究

车载跟瞄设备对低轨卫星进行测量时,常采用轨道预报获得卫星理论运行轨迹,光测设备以预报值作为引导值,搜索目标进行跟踪.跟踪过程中,由于云雾遮蔽或地影的影响,会使光测设备丢失目标.这时可采用理论引导的方式,利用预报轨道继续搜索跟踪.但由于轨道预报的误差较大,且目标过境时间有限,因此这种方法存在一定的缺陷.探讨利用修正目标跟踪脱靶量以及光测设备轴系误差后的实时跟踪值作为已知条件,采用改进的拉普拉斯方法对目标位置进行实时预测,解决上述问题.通过实时预测误差与轨道误差的比较,证明该方法的有效性.     

春季平均气温的多层递阶长期预报

本文以D.G.Lainiotis在估计理论方面的工作为理论依据（文献[1]），运用文献[2][4][5]中给出的方法，扼要介绍了一种高精度自适应预报－－多层递阶预报在春季平均气温长期预报上的应用。它克服了通常方法中因固定参数模型所造成较大误差的缺陷，在预报中充分考虑到系统的时变特性，将预报问题分害成对系统的时变参数预报及对系统的状态（输出）预报两部分。文章利用了黑龙江省某地区1961－1989年二十九年的春季平均气温的历史资料，对1990－2010年二十一个年份的春季平均气温进行了长期预报。这一测试结果表明这一方法比其它方法更准确。     

基于灰色马尔可夫预测模型的农村人均收入预测

以武汉市农村人均收入为样本,将灰色预测模型和马尔可夫链预测模型相结合,通过对比预测的数据信息与实际数据信息差距,对2011年和2012年武汉市农村人均收入进行了预测计算.根据相关模拟检验与残差修正,灰色马尔可夫链可视为农村人均收入预测的可行且有效的方法.结果显示,单纯地运用灰色模型,预测值与实际值的误差均值是0.687%;通过马尔可夫链模型的二次模拟得到的误差明显减小.     

气象因素与儿童气管炎关系的研究

利用济南市卫生局提供的2003年儿童气管炎患者的病例资料和济南市气象局提供的2003年气象资料,对济南地区儿童气管炎疾病与气象资料的关系进行研究.通过相关性分析,用多元回归方法建立了儿童气管炎发病人数的日预报方程.然后对数据进行处理,按季节建立了儿童气管炎与气象要素之间的日预报方程.     

新安江流域5—9月间汛期雨量预报的混合回归模型

应用回归模型拟合,是考虑因变量与自变量之间的统计依赖关系,借以预报因变量的未来值.而自回归模型拟合,是考虑因变量自身前后的统计依赖关系,借以预报序列的未来取值.前者没有利用因变量自身的统计依赖关系,后者没有利用因变量与自变量之间的统计依赖关系.在雨量预报中,雨量的多寡,有时不仅与所选自变量有关,而且与预报量的历史取值亦有很大联系.混合回归模型综合了以上两种考虑,克服了单纯回归模型存在的不足。     

非齐次小周期复合材料稳态热问题的多尺度渐近展开和收敛性分析

利用多尺度渐近展开和均匀化思想讨论了小周期复合材料的稳态热问题,得到了非齐次边界条件下二阶椭圆型方程的渐近解,并给出了原始解与渐近解之间的误差估计,数值结果表明了结论的正确性.     

ARMA模型在哈尔滨气温预测中的应用

将时间序列分析引入到气温时间序列预测的研究中,深入分析气温样本数据,并对其建立ARMA模型.采用最佳准则函数法确定模型的阶数,并利用自相关函数对模型的残差进行了检验.通过条件期望预测和适时修正预测方法求得预测值,与真实值的比较得到适时修正预测精确度比条件期望预测的精确度高.     

AN ADAPTIVE LEARNING FRAMEWORK FOR FORECASTING SEASONAL WATER ALLOCATIONS IN IRRIGATED CATCHMENTS

Abstract This paper describes an adaptive learning framework for forecasting end‐season water allocations using climate forecasts, historic allocation data, and results of other detailed hydrological models. The adaptive learning framework is based on artificial neural network (ANN) method, which can be trained using past data to predict future water allocations. Using this technique, it was possible to develop forecast models for end‐irrigation‐season water allocations from allocation data available from 1891 to 2005 based on the allocation level at the start of the irrigation season. The model forecasting skill was further improved by the incorporation of a set of correlating clusters of sea surface temperature (SST) and the Southern oscillation index (SOI) data. A key feature of the model is to include a risk factor for the end‐season water allocations based on the start of the season water allocation. The interactive ANN model works in a risk‐management context by providing probability of availability of water for allocation for the prediction month using historic data and/or with the incorporation of SST/SOI information from the previous months. All four developed ANN models (historic data only, SST incorporated, SOI incorporated, SST‐SOI incorporated) demonstrated ANN capability of forecasting end‐of‐season water allocation provided sufficient data on historic allocation are available. SOI incorporated ANN model was the most promising forecasting tool that showed good performance during the field testing of the model.     

MODELING THE EFFECT OF SEA SURFACE TEMPERATURE ON SEA TURTLE NESTING ACTIVITIES BY INVESTIGATING SEASONAL TRENDS

ABSTRACT. In the present study, a series of models have been developed in order to investigate the effect of sea water temperature upon the nesting activities of marine turtles. Autoregressive integrated moving average (ARIMA) models, ARIMA models with transfer functions and regression models were developed for forecasting variations in the breeding activity of loggerheads, nesting at the island of Zakynthos, West Greece. Identification and development of the models was determined by the use of several statistical criteria. Weekly data series of sea turtles emerging attempts and number of nests laid were analyzed and compared with sea surface temperature (SST) data series. Our results indicate that whether SST data were included in the ARIMA models with transfer functions and the regression models that developed to describe both emergence data and number of nests, tended to improve fitting and forecasting accuracy. Data series of the number of nests laid was further correlated with observation of emergence data. Adding the effect of previous and current year nesting attempts and including SST data resulted to higher forecasting accuracy and fitting performance.     

中美双边航空运输市场组合预测研究

预测应用研究表明,组合预测可以综合利用各单项预测方法所提供的信息,是提高预测精度的有效途径.本文在平均发展速度预测法、指数趋势预测以及灰色预测方法的基础上建立组合预测模型,采用熵值法确定组合权系数,预测了2006年至2010年中美间航空运输周转量、中美间航空客运量及货运量.     

BP神经网络算法在需水预测与评价中的应用

需水预测是进行水资源规划与管理的必备技术方法,在水资源短缺的地区显得尤为重要.合理而准确地预测未来的需水量,可以避免投资的浪费或减少将来用水危机的发生.以江西省为例,采用BP神经网络算法对江西省近期的需水进行预测与评价,将结果与其它方法预测的进行比较,比较结果说明神经网络算法预测需水是成功的。     

灰色时序组合模型及其在地下水埋深预测中的应用

地下水埋深的变化过程是一个复杂的非线性过程,这种具有复杂的非线性组合特征的序列,使用某一种模型进行预测,结果往往不理想.在分析了灰色GM(1,1)模型、灰色GM(1,1)周期性修正模型和时序AR(n)模型的优点和缺点基础上,提出了一种新的灰色时序组合预报模型.该方法利用了GM预测所需原始数据少、方法简单的优点,用周期修正方法反映其地下水位埋深周期性波动的特征,用AR(n)模型预报其地下水位埋深的随机变化.实例研究表明,这种方法方便简洁实用且预测结果接近于实际观测值,为其它地区的地下水位埋深和相关时间序列的分析研究提供参考与借鉴作用.     

深圳市供水量的最优组合预测

城市供水系统是一个复杂的大系统,供水量受多种因素的共同影响。本文以深圳市最近20多年的供水量历史数据为基础,建立了深圳市供水量的最优组合预测模型。该模型具有较高的预测精度,组合预测的预测效果优于任意一种单一预测的预测效果,供水量预测结果对深圳市未来供水的短期或长期规划能起到重要的宏观指导作用。     

An extended grey forecasting model for omnidirectional forecasting considering data gap difference

To achieve effective and efficient decision making in a highly competitive business environment, an enterprise must have an appropriate forecasting technique that can meet the requirements of both timeliness and accuracy. Accordingly, in the early stages, building a forecasting model with incomplete information and limited samples is very important to a business. Grey system theory is one of the prediction methods that can be built with a small sample and yet has a strong ability to make short-term predictions. The purpose of this study is to come up with an improved forecasting model based on the concept of this theory to enlarge the applicability of the grey forecasting model in various situations. By extending the data transforming approach, this method generalizes a building procedure for the grey model to grasp the data outline and information trend. Specifically, a novel inverse accumulating generation operator is developed to enable omnidirectional forecasting. The research utilizes observations of the titanium alloy fatigue limit along with temperature changes as raw data to verify the performance of the proposed method. The experimental results show that not only can this method expand the application scope of the grey forecasting model, but also improve its forecasting accuracy.     

MODELING AND FORECASTING MONTHLY AVERAGE WATER LEVELS BASED ON THE ELLIPTIC ORBIT MODEL

Evaluation and forecasting of water‐level fluctuation for one river is of increasing importance since it is intimately associated with human welfare and socioeconomic sustainability development. In this study, it is found that time series of monthly water‐level fluctuation exhibits annual cyclical variation. Then with annual periodic extension for monthly water‐level fluctuation, the so‐called “elliptic orbit model” is proposed for describing monthly water‐level fluctuation by mapping its time series into the polar coordinates. Experiments and result analysis indicate potentiality of the proposed method that it yields satisfying results in evaluating and forecasting monthly water‐level fluctuation at the monitoring stations in the Yangtze River of China. It is shown that the monthly water‐level fluctuation is well described by the proposed elliptic orbit model, which offers a vivid approach for modeling and forecasting monthly water‐level fluctuation in a concise and intuitive way.     

The Relation between Gas Demand and Temperature: A Study in Statistical Demand Forecasting

The paper describes the methods of relating gas demand to temperature which have previously been used for forecasting gas demand in the North Western Area, and introduces a new approach which involves the use of exponential weighting as applied to the average temperatures occurring in the 4 weeks prior to the forecast. An analysis of previous gas demand experience (in relation to actual temperatures throughout) showed that the new method gave a much improved fit to the data, and that this accuracy was maintained even in prolonged periods of abnormal weather conditions. The new method also threw more light on the effect on gas demand of other meteorological factors apart from temperature, and showed up significant effects which had not been detected when using other methods. The method can be used for forecasting on both a daily and a weekly basis, and is considered to represent a significant step forward in the technique of gas demand forecasting.