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目前中国以化石能源为主的能源消费格局正逐步优化,清洁能源的消费规模逐步增加。对天然气消费量的预测分析对未来能源消费结构调整具有积极意义。本文创新性地采用拟合值偏离度倒数法进行权重设置,利用残差自回归模型和Kalman滤波算法构建组合预测模型,以《BP世界能源统计年鉴》和《中国统计年鉴》1980–2017年的天然气消费历史数据为对象,对中国天然气消费量进行预测研究。研究结果表明:(1)组合预测模型的预测精度更高:残差自回归预测模型的相对误差落在(–0.08,0.09)区间内,卡尔曼滤波预测的相对误差落在(–0.09, 0.32)区间内,组合预测模型相对误差落在(–0.03, 0.11)区间内。(2)组合预测模型预测结果的稳定性更好:残差自回归预测模型相对误差的预测方差为0.002,卡尔曼滤波预测相对误差的预测方差为0.007,组合预测模型相对误差的预测方差为0.001。(3)其他条件不变的情况下,2018年天然气消费量费预测值为2418.08亿m~3。与其他时间序列预测方法相比,利用残差自回归模型和卡尔曼滤波算法构成的组合预测模型对数据限制条件少,可操作性强,且分析结果更为可信。 相似文献
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将集合预报成员不等权重思想与集合卡尔曼滤波(EnKF)同化方法相结合,利用集合成员的离散度作为权重因子,对EnKF算法优化后的集合成员采用不等权重取平均值,作为同化后的预报值。首先检验了集合离散度和预报误差的相关性,证明将集合离散度作为权重因子的可靠性;利用一个水文过程模型(DHSVM)和实测数据进行了土壤水分的同化变权实验,对EnKF分析和更新后产生的土壤水分集合,分别采用算术平均和变权平均的方法,计算土壤水分预报结果并进行比较。实验表明,集合变权平均法可以进一步提高同化的预报效果。 相似文献
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区域生态安全评价的熵组合权重属性识别模型 总被引：23，自引：2，他引：21
为合理地确定区域生态安全评价的指标权重,提出把指标的权重结构分为反映评价指标不同属性对生态安全等级的影响程度的主观权重,和反映各区域评价指标样本值差异信息对生态安全等级的影响程度的客观权重,可分别采用层次分析法和熵权法确定这些权重,再用最小相对信息熵原理把它们综合为组合权重;为体现单指标评价过程中的评价作用,提出用属性识别模型进行单指标评价的新思路;对组合权重值和单指标评价值进行相乘并累加,建立了区域生态安全评价的熵组合权重属性识别模型(AR-CWE)。结果说明:用AR-CWE既可利用指标中的专家主观经验信息,又可挖掘各区域评价指标样本值的客观差异信息,权重信息利用全面,评价结果合理,方法通用,在安全系统综合评价中具有一定的应用价值。 相似文献
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气候系统是较典型的灰色系统，适于应用灰色系统理论预测模型进行预测。东川是泥石流高发区，降水是促发泥石流的动力条件，是泥石流预报的重要依据。基于1981-2003年的降水资料，应用灰色系统（1，1）预测模型，对东川地区未来年份的降水量进行了灰色预测，预测效果良好，可为当地气象部门、农业部门及防灾减灾部门提供科学的依据。 相似文献
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小城镇灾害易损性熵权与可变模糊集评估方法研究 总被引：1，自引：0，他引：1
姜云 《地理与地理信息科学》2009,25(6)
为了客观评估小城镇的灾害易损性,提出一种熵权和可变模糊集组合评估方法.采用熵值法确定小城镇灾害易损性评估指标的权重,采用可变模糊集理论建立小城镇灾害易损性评估模型,并以湖南省小城镇为例,进行了相关研究.研究表明:熵值法通过挖掘统计数据的熵来确定评估指标的熵权,所确定的权重是客观的;可变模糊集通过相对隶属度和相对差异函数确定综合相对隶属度,并通过参数组合变换验证了评估方法的可靠性.因而熵权和可变模糊集组合评估是小城镇灾害易损性评估的一种有效方法. 相似文献
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大多数旅游需求预测研究是基于目的地游客总数或消费总量开展的,尚未按不同的旅游目的或客源地细分进行预测.以天津欢乐谷主题公园为案例地,选择2014年第40周到2015年第26周为研究时段,利用通信大数据,提出了一种面向客源地的聚类-ARIMA组合预测模型.通过对不同客源地的时序数据进行聚类,选取各类别中的代表性客源地分别构建ARIMA预测模型.结果表明:对欢乐谷主题公园各客源地分别建模与聚类后通过6个代表客源地建模得到的结果一致;后者可以降低80%的预测成本.该方法具有较高的预测精度和较低的计算成本,适合面向客源地的短期旅游需求预测,可为旅游目的地提供更具针对性的旅游需求管理、分析与决策支撑. 相似文献
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基于支持向量回归机的耕地保有量组合预测 总被引：1，自引：0，他引：1
赖红松 《地理与地理信息科学》2011,27(2):56-60
为提高耕地保有量预测精度,将灰色预测GM(1,1)模型、动力预测模型、BP网络预测模型和加权支持向量回归机预测模型相结合,建立了基于支持向量回归机的耕地保有量组合预测模型,并将其用于温州市耕地保有量预测。结果表明,该模型比任一单一预测模型精度更高,可用于耕地保有量预测。 相似文献
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为验证贝叶斯模式平均（BMA）在东江流域的适用性,基于TIGGE多模式集合预报资料对东江流域不同预报时段（1、3、5及10日）以及不同地域（上、中及下游）应用BMA,得到的主要结论为：1）BMA的预报能力在时空上都较为稳定,预报效果较好;2）在各预报时段中BMA极大提高了预报的精度,其中预报时段越短,误差减少的幅度越大;3）当95%分位数已超过警戒的雨量时,应做好相关的预警和预防工作,但BMA也会出现误报和虚报的情况,需要进行权衡和风险的评估,同时这也是需进一步研究和完善的地方;4）结合其他国内外的研究来看,BMA对天气要素预报的订正作用可能具有普适性,值得推广使用。 相似文献
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Qunying Huang Karl Benedict Abdelmounaam Rezgui Jibo Xie Jizhe Xia 《International journal of geographical information science》2013,27(4):765-784
Forecasting dust storms for large geographical areas with high resolution poses great challenges for scientific and computational research. Limitations of computing power and the scalability of parallel systems preclude an immediate solution to such challenges. This article reports our research on using adaptively coupled models to resolve the computational challenges and enable the computability of dust storm forecasting by dividing the large geographical domain into multiple subdomains based on spatiotemporal distributions of the dust storm. A dust storm model (Eta-8bin) performs a quick forecasting with low resolution (22 km) to identify potential hotspots with high dust concentration. A finer model, non-hydrostatic mesoscale model (NMM-dust) performs high-resolution (3 km) forecasting over the much smaller hotspots in parallel to reduce computational requirements and computing time. We also adopted spatiotemporal principles among computing resources and subdomains to optimize parallel systems and improve the performance of high-resolution NMM-dust model. This research enabled the computability of high-resolution, large-area dust storm forecasting using the adaptively coupled execution of the two models Eta-8bin and NMM-dust. 相似文献
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According to the principle of the eruption of debris flows, the new torrent classification techniques are brought forward.
The torrent there can be divided into 4 types such as the debris flow torrent with high destructive strength, the debris flow
torrent, high sand-carrying capacity flush flood torrent and common flush flood by the techniques. In this paper, the classification
indices system and the quantitative rating methods are presented. Based on torrent classification, debris flow torrent hazard
zone mapping techniques by which the debris flow disaster early-warning object can be ascertained accurately are identified.
The key techniques of building the debris flow disaster neural network (NN) real time forecasting model are given detailed
explanations in this paper, including the determination of neural node at the input layer, the output layer and the implicit
layer, the construction of knowledge source and the initial weight value and so on. With this technique, the debris flow disaster
real-time forecasting neural network model is built according to the rainfall features of the historical debris flow disasters,
which includes multiple rain factors such as rainfall of the disaster day, the rainfall of 15 days before the disaster day,
the maximal rate of rainfall in one hour and ten minutes. It can forecast the probability, critical rainfall of eruption of
the debris flows, through the real-time rainfall monitoring or weather forecasting. Based on the torrent classification and
hazard zone mapping, combined with rainfall monitoring in the rainy season and real-time forecasting models, the debris flow
disaster early-warning system is built. In this system, the GIS technique, the advanced international software and hardware
are applied, which makes the system’s performance steady with good expansibility. The system is a visual information system
that serves management and decision-making, which can facilitate timely inspect of the variation of the torrent type and hazardous
zone, the torrent management, the early-warning of disasters and the disaster reduction and prevention. 相似文献
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基于CMIP5模式的干旱内陆河流域未来气候变化预估 总被引：3，自引：1，他引：2
我国西北干旱半干旱地区水资源短缺、生态环境脆弱,未来气候变化预估对水资源管理具有重要的现实意义。以黑河流域为研究区,基于1960-2014年月值NCEP再分析资料与气象要素实测资料,建立逐步回归降尺度模型;针对模型不足,提出一种补充逐步回归降尺度模型;经2006-2014年CMIP5中CNRM-CM5模式的区域适用性评价,选取适宜模型进行2016-2060年CNRM-CM5模式下的流域未来气候变化预估。主要结论为：（1）补充逐步回归模型的模拟效果总体要好于逐步回归模型,两模型对流域气温的模拟效果要好于降水。（2）降尺度模型的CNRMCM5模式适用性评价表明,RCP4.5与RCP8.5路径下,补充回归模型的适用性总体好于逐步回归模型。（3）两种路径下,黑河流域上中游未来年均降水量分别为324.94 mm、330.15 mm,未来流域降水分布的不均匀性增强。（4）两种路径下黑河流域中下游未来年均气温分别为10.25℃、10.77℃。 相似文献
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According to the principle of the eruption of debris flows, the new torrent classification techniques are brought forward. The torrent there can be divided into 4 types such as the debris flow torrent with high destructive strength, the debris flow torrent, high sand-carrying capacity flush flood torrent and common flush flood by the techniques. In this paper, the classification indices system and the quantitative rating methods are presented. Based on torrent classification, debris flow torrent hazard zone mapping techniques by which the debris flow disaster early-warning object can be ascertained accurately are identified. The key techniques of building the debris flow disaster neural network (NN)real time forecasting model are given detailed explanations in this paper, including the determination of neural node at the input layer, the output layer and the implicit layer, the construction of knowledge source and the initial weight value and so on. With this technique, the debris flow disaster real-time forecasting neural network model is built according to the rainfall features of the historical debris flow disasters, which includes multiple rain factors such as rainfall of the disaster day, the rainfall of 15 days before the disaster day, the maximal rate of rainfall in one hour and ten minutes. It can forecast the probability, critical rainfall of eruption of the debris flows, through the real-time rainfall monitoring or weather forecasting. Based on the torrent classification and hazard zone mapping, combined with rainfall monitoring in the rainy season and real-time forecasting models, the debris flow disaster early-warning system is built. In this system, the GIS technique, the advanced international software and hardware are applied, which makes the system′s performance steady with good expansibility. The system is a visual information system that serves management and decision-making, which can facilitate timely inspect of the variation of the torrent type and hazardous zone, the torrent management, the early-warning of disasters and the disaster reduction and prevention. 相似文献
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According to the principle of the eruption of debris flows, the new torrent classification techniques are brought forward. The torrent there can be divided into 4 types such as the debris flow torrent with high destructive strength, the debris flow torrent, high sand-carrying capacity flush flood torrent and common flush flood by the techniques. In this paper, the classification indices system and the quantitative rating methods are presented. Based on torrent classification, debris flow torrent hazard zone mapping techniques by which the debris flow disaster early-warning object can be ascertained accurately are identified. The key techniques of building the debris flow disaster neural network (NN) real time forecasting model are given detailed explanations in this paper, including the determination of neural node at the input layer, the output layer and the implicit layer, the construction of knowledge source and the initial weight value and so on. With this technique, the debris flow disaster real-time forecasting neural network model is built according to the rainfall features of the historical debris flow disasters, which includes multiple rain factors such as rainfall of the disaster day, the rainfall of 15 days before the disaster day, the maximal rate of rainfall in one hour and ten minutes. It can forecast the probability, critical rainfall of eruption of the debris flows, through the real-time rainfall monitoring or weather forecasting. Based on the torrent classification and hazard zone mapping, combined with rainfall monitoring in the rainy season and real-time forecasting models, the debris flow disaster early-warning system is built. In this system, the GIS technique, the advanced international software and hardware are applied, which makes the system's performance steady with good expansibility. The system is a visual information system that serves management and decision-making, which can facilitate timely inspect of the variation of the torrent type and hazardous zone, the torrent management, the early-warning of disasters and the disaster reduction and prevention. 相似文献
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提高太阳辐射短时临近预报（<6 h）的准确率是确保电网调度的重要举措,也是极具挑战性的技术瓶颈之一。基于云-辐射关系,利用地面观测的太阳辐照度反演的云相对辐射强迫比值,构建了太阳辐射短时临近预报模型（R模型）,并用美国南部大平原中心站16 a的辐照度观测数据,对R模型的预报性能进行了评估。结果表明：（1）有云存在的个例中,R模型较传统的简单持续性模型（Simple模型）的预报性能有很大提升,相比于预报性能较高的智能持续性模型（Smart模型或RCRF模型）仍有2%～25%的改进。（2）在16 a包含2.9×105个8类云状个例的总体检验中,当预报时效超过1 h时,R模型的预报性能显著优于Simple模型和RCRF模型。相对于RCRF模型,R模型在6 h预报时效下,对总辐射和直接辐射的预报性能可分别提高25%和19%,预报时效分别延长了1.5 h和1 h。（3）R模型为太阳辐射短时临近预报提供了准确率更高的基准模型。同时,该模型可仅依靠地面短期的辐照度观测资料即可预报,为缺少同期气象要素观测的光伏电厂的辐射预报提供了新的途径或新的可能。 相似文献