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1.
这篇文章旨在构建一个期权定价模型以减少与埃塞俄比亚咖啡价格波动相关的风险。我们使用从埃塞俄比亚商品交易所(ECX)获得的2011年5月31日至2018年3月30日期间记录的埃塞俄比亚每日(WSDA3)咖啡价格来分析其咖啡价格的波动。本文使用跳跃扩散模型对咖啡价格进行建模和期权定价,应用最大似然法估计模型参数,使用均方根误差(RMSE)来对模型进行验证。结果表明Merton和双指数跳跃扩散模型的RMSE值分别为0.1093和0.0783,模型模拟结果与实际数据非常吻合,说明采用蒙特卡罗技术得到的WSDA3价格来对期权定价时,双指数跳跃扩散模型比Merton模型更为有效。 相似文献
2.
基于卡尔曼滤波算法的焦炭价格预测 总被引：1，自引：1，他引：0
焦炭价格预测研究具有重要的理论和实践意义,本文利用卡尔曼滤波算法对焦炭价格进行预测研究。建立状态空间模型时,选取焦炭价格作为唯一的状态变量,通过每一时刻变量观测值与预测值形成的新息,不断更新和迭代,以寻求最优估测值。实证分析表明,该算法对焦炭价格的跟踪和预测效果较好。 相似文献
3.
Forecasting Gas Consumption Based on a Residual Auto-Regression Model and Kalman Filtering Algorithm
Consumption of clean energy has been increasing in China. Forecasting gas consumption is important to adjusting the energy consumption structure in the future. Based on historical data of gas consumption from 1980 to 2017, this paper presents a weight method of the inverse deviation of fitted value, and a combined forecast based on a residual auto-regression model and Kalman filtering algorithm is used to forecast gas consumption. Our results show that: (1) The combination forecast is of higher precision: the relative errors of the residual auto-regressive model, the Kalman filtering algorithm and the combination model are within the range (-0.08, 0.09), (-0.09, 0.32) and (-0.03, 0.11), respectively. (2) The combination forecast is of greater stability: the variance of relative error of the residual auto-regressive model, the Kalman filtering algorithm and the combination model are 0.002, 0.007 and 0.001, respectively. (3) Provided that other conditions are invariant, the predicted value of gas consumption in 2018 is 241.81×10 9 m 3. Compared to other time-series forecasting methods, this combined model is less restrictive, performs well and the result is more credible. 相似文献
4.
基于高光谱数据的天山北坡积雪孔隙率反演研究 总被引：1，自引：1，他引：0
以新疆天山北坡中段典型流域季节性积雪为研究对象,基于高光谱遥感监测技术,分析了融雪期积雪孔隙率与光谱反射率的相关性。采用偏最小二乘法(PLS)对相关性较高的波段进行压缩,并提取贡献率最高的前四个主成分,以此用来确定神经网络的隐含节点数、输入层、输出层的初始权值,建立PLS-BP模型进行积雪孔隙率反演研究。结果表明:当隐含节点数为3,模型的线性确定相关系数(R2)较高为0.9159,RMSE为0.04,相对误差为0.23。与传统偏最小二乘回归(PLSR)、主成分回归(PCA)建模方法相比,精度较高,所建定量模型可用于高光谱遥感反演积雪孔隙率。 相似文献
5.
积雪是新疆地区重要的水源补给,是冰冻和融雪洪水灾害的直接原因,也是水资源管理、气候变化、灾害防治和融雪模拟预报的主要参数。针对多种积雪信息提取方法的优缺点,提出运用特征空间方法,构建积雪丰度反演模型,并与支持向量机提取积雪丰度进行精度对比分析,NA模型方法的相关系数（R2）值比支持向量机方法高2.4百分点,而均方根误差（RMSE）提高了0.106。结果表明：利用归一化差分积雪指数（NDSI）和反照率（Albedo）建立二维特征空间反演积雪丰度的方法是可行的,并且提取精度优于支持向量机（SVM）方法。因此,该方法对水资源管理、气候变化以及洪水模拟预测等方面的研究具有一定参考意义。 相似文献
6.
Recent upward trends in acres irrigated have been linked to increasing near-surface moisture. Unfortunately, stations with dew point data for monitoring near-surface moisture are sparse. Thus, models that estimate dew points from more readily observed data sources are useful. Daily average dew temperatures were estimated and evaluated at 14 stations in Southwest Georgia using linear regression models and artificial neural networks (ANN). Estimation methods were drawn from simple and readily available meteorological observations, therefore only temperature and precipitation were considered as input variables. In total, three linear regression models and 27 ANN were analyzed. The two methods were evaluated using root mean square error (RMSE), mean absolute error (MAE), and other model evaluation techniques to assess the skill of the estimation methods. Both methods produced adequate estimates of daily averaged dew point temperatures, with the ANN displaying the best overall skill. The optimal performance of both models was during the warm season. Both methods had higher error associated with colder dew points, potentially due to the lack of observed values at those ranges. On average, the ANN reduced RMSE by 6.86% and MAE by 8.30% when compared to the best performing linear regression model. 相似文献
7.
C. J. Willmott K. Matsuura 《International journal of geographical information science》2013,27(1):89-102
Spatial cross‐validation and average‐error statistics are examined with respect to their abilities to evaluate alternate spatial interpolation methods. A simple cross‐validation methodology is described, and the relative abilities of three, dimensioned error statistics—the root‐mean‐square error (RMSE), the mean absolute error (MAE), and the mean bias error (MBE)—to describe average interpolator performance are examined. To illustrate our points, climatologically averaged weather‐station temperatures were obtained from the Global Historical Climatology Network (GHCN), Version 2, and then alternately interpolated spatially (gridded) using two spatial‐interpolation procedures. Substantial differences in the performance of our two spatial interpolators are evident in maps of the cross‐validation error fields, in the average‐error statistics, as well as in estimated land‐surface‐average air temperatures that differ by more than 2°C. The RMSE and its square, the mean‐square error (MSE), are of particular interest, because they are the most widely reported average‐error measures, and they tend to be misleading. It (RMSE) is an inappropriate measure of average error because it is a function of three characteristics of a set of errors, rather than of one (the average error). Our findings indicate that MAE and MBE are natural measures of average error and that (unlike RMSE) they are unambiguous. 相似文献
8.
采用基于风条纹提取风向的方式，利用地球物理模式函数，基于Sentinel-1A数据，通过CMOD5模型反演2017年3、5、7、12月份广东省近海海域风场。将反演结果与实测数据对比，风速普遍比实测风速大，风速反演的平均绝对误差为1.98 m/s，均方根误差为2.74 m/s，相关系数为0.8。其中3、5、7月的风速较为接近，且平均绝对误差和均方根误差都＜2 m/s，而12月份平均风速＞8 m/s，实测数据与卫星过境时间不完全匹配，导致平均绝对误差和均方根误差都偏大。哨兵一（Sentinel-1A）影像反演结果整体上与实测数据相一致，验证了COMD5反演模型适用于广东省近海高分辨率海洋风场反演，可为下一步估算广东省风能资源储量提供可能。 相似文献
9.
Mahsa Hasanpour Kashani Mohammad Ali Ghorbani Yagob Dinpashoh Sedaghat Shahmorad 《Natural Resources Research》2014,23(3):341-354
This study evaluates the performances of two distinct linear and non-linear models for simulating non-linear rainfall–runoff processes and their applications to flood forecasting in the Navrood River basin, Iran. Due to the excellent capacity of the artificial neural networks [multilayer perceptron (MLP)] and Volterra model, these models were used to approximate arbitrary non-linear rainfall–runoff processes. The MLP model was trained using two different training algorithms. The Volterra model was applied as a linear model [the first-order Volterra (FOV) model] and solved using the traditional ordinary least-square (OLS) method. Storm events within the Navrood River basin were used to verify the suitability of the two models. The models’ performances were evaluated and compared using five performance criteria namely coefficient of efficiency, root mean square error, error of total volume, relative error of peak discharge, and error of time for peak to arrive. Results indicated that the non-linear MLP models outperform the linear FOV model. The latter was ineffective because of the non-linearity of the rainfall–runoff process. Moreover, the OLS method is inefficient when the FOV model has many parameters that must be estimated. 相似文献
10.
基于IBIS模型对中国1955~2006年的土壤上层1m的年平均与月平均土壤温度进行模拟,并利用全国气象站点土壤温度观测数据对模拟结果进行验证,结果显示中国南方区的模拟效果优于北方及青藏高原区,春、夏、秋三季模拟效果优于冬季,总体而言取得了较满意的效果。基于模拟结果,利用Mann-Kendall方法对中国1955~2006年年平均和月平均土壤温度进行趋势分析的结果表明,年平均土壤温度,中国北方呈显著上升趋势,南方呈非显著上升趋势,四川盆地、贵州中部、藏东南及天山地区等小部分区域呈现显著或非显著下降趋势;月平均土壤温度,北方基本保持显著上升趋势,南方地区7~9月份总体呈现出下降的趋势,8月份最为显著。 相似文献
11.
基于ETM+影像的草地退化评价模型研究——以西藏自治区那曲县为例 总被引：8，自引：5，他引：3
在实地调查和样方测定基础上,选用建群种植株高度、草地植被盖度和草地生物量作为草地退化地面评价体系中的单一评价指标,并通过加权综合三个单一评价指标,构建草地退化地面综合评价指标;在分析退化草地光谱特征的基础上,从ETM+影像的波段数据中派生出草地退化的遥感评价指标。通过相关分析的方法,选出最能反映草地退化趋势、最适于用于线性拟合的地面评价指标（地面综合评价指标）和遥感评价指标（TM4/TM5）,采用线性回归技术构建草地退化遥感评价模型,并通过计算确定系数（R2）、均方根差（RMSE）和相对误差对模型精度做出评价。确定主要草地类型的不同退化等级标准,完成草地退化图的编制。通过分析草地退化地面评价指标与遥感评价指标之间的关系,探讨了西藏北部草地退化的遥感评价模型,为科学、快速评价草地退化提供一种新思路。 相似文献
12.
海冰具有良好的热力隔绝效应,它通过影响海洋和大气的热交换进而影响全球的气候变化。海冰密集度是极区海冰研究的重要指标之一。为实现高空间分辨率多类型海冰密集度的估算,本文将亮温极化梯度率和光谱梯度率引入基于全约束最小二乘法(fully constrained least squares,FCLS)的海冰密集度估算方法,并利用南极海冰过程与气候计划(Antarctic Sea Ice Processes and Climate,ASPe Ct)对改进方法的精度进行验证,然后与NASA Team2(NT2)算法和ARTIST Sea Ice(ASI)算法获得的海冰密集度结果进行了对比分析。结果显示,3种算法中本研究的方法精度最高,全年均方差13.8%,偏差为-0.7%;改进的方法对多年冰的估算精度优于一年冰。 相似文献
13.
近年来城市暴雨出现突发和多发态势,导致城市内涝灾害频繁发生,威胁着城市居民的生命和财产安全。随着城市降雨积水监测网的建立,获得分钟尺度的降雨和积水时序监测数据成为可能,实现了城市内涝的实时监控。但目前对监测数据的利用仍显不足,缺乏对其深度分析挖掘,造成监测系统“只监不控”的局面。本文基于城市降雨积水监测网的监测数据,根据积水时间相关性、降雨空间相关性以及降雨积水序列相关性,构建降雨积水的时空自相关移动平均模型(STARMA),对城市暴雨积水点积水过程进行短时预测。STARMA模型已被广泛应用于交通预测、环境变量预测以及社会经济领域,特别是在时空过程机理不清楚、多因素时空变量影响的情况下效果较好。本文首次将该模型应用到降水积水过程拟合和积水短时预测上,同时在方法上改进了传统单变量的STARMA模型,建立降雨和积水双变量的STARMA模型模拟降雨积水过程。并以北京市2012年“7.21”事件降雨积水过程为研究对象,以丰北桥、花乡桥、马家楼桥和六里桥4个积水监测点为例,建立降雨积水的STARMA模型,以5 min为步长作积水5、10、15 min三步预测。验证结果表明,该模型在降雨积水过程中拟合效果较好,模型短时预测精度较高。该项研究能够有效地利用监测数据,提高信息预警和应急指挥能力,为市政防汛或交通等部门提供决策支持。 相似文献
14.
Cort J. Willmott 《自然地理学》2013,34(2):184-194
Traditional methods of evaluating geographic models by statistical comparisons between observed and simulated variates are criticized. In particular, it is suggested that the correlation coefficient (r), its square and tests of their statistical significance are inadequate for such purposes. The root mean squared error (RMSE) and related measures as well as a new index of agreement (d) are alternatively presented as superior indices for making such comparisons. Arguments are made for increasing the number of digital algorithms and data plots being published. 相似文献
15.
流域输沙过程是地貌学和地表动力学的重要研究内容,但传统的输沙过程监测方法仅能得到某个区域的总输沙率,无法推算其空间分布。论文以黄土高原绥德县窑家湾小流域为例,利用无人机摄影测量技术得到其2006年和2019年2期数字高程模型(DEM)并计算地形变化量;然后,根据质量守恒原理和多流向算法建立泥沙在空间上的输送模型,进而计算小流域输沙率的空间分布。实验结果表明,该模型能有效模拟泥沙在空间上的输送情况,输沙率出现质量不守恒的区域面积占比小于4%,且不守恒区域多为人类活动影响区。同时,论文讨论了DEM的选择和不同地形变化检测水平对模型结果的影响。当使用第一期DEM进行泥沙搬运路径推算时,质量不守恒区域的面积显著降低。使用误差空间分布图进行地形变化检测得到的输沙率结果鲁棒性更强。使用中误差进行地形检测得到的结果在不同置信度下变化较大。基于无人机地形变化检测的空间输沙模型能方便、快捷地提供详尽的输沙率空间分布,为地表过程研究带来了新的机遇。 相似文献
16.
开展干旱预测是有效应对干旱风险的前提基础。利用1958—2017年青海省38个气象站点逐日降水量数据计算多尺度标准化降水指数（SPI）,并建立了SPI序列自回归移动平均模型（ARIMA）、长短时记忆神经网络模型（LSTM）和基于二者优点提出的ARIMA-LSTM组合模型;对模型参数进行率定和验证后,利用所建立的模型,以西宁站点为例,对多尺度SPI值进行预测,借助均方根误差（RMSE）、平均绝对百分比误差（MAPE）和决定系数R2对所有预测模型的有效性进行判定。结果表明：ARIMA-LSTM组合模型在SPI1和SPI12的RMSE值分别为0.159 7和0.181 0,均低于ARIMA模型的1.265 4和0.293 3,说明ARIMA模型与ARIMA-LSTM组合模型对SPI的预测精度都与时间尺度有关,ARIMA模型的预测精度随着时间尺度的增加而逐渐提高;结合GIS并利用实测数据与模型的预测数据相比较说明ARIMA-LSTM组合模型相比于单一ARIMA模型的预测精度更高,且能够很好拟合不同时间尺度的SPI值。 相似文献
17.
快速获取区域土壤盐渍化程度信息,对于盐渍化治理与生态环境保护具有重要意义。以银川平原为研究区,以盐分影响因子和盐分指数分别作为输入参数,建立支持向量机(SVM),BP神经网络(BPNN)和贝叶斯神经网络(BNN)3种土壤盐分预测模型,选取最佳模型进行研究区不同深度的土壤盐渍化预测。结果表明：(1)0～20 cm土壤盐分预测模型中基于影响因子变量组的BNN模型效果最佳,决定系数(R2)为0.618,均方根误差(RMSE)为2.986;20～40 cm土壤盐分预测模型中基于盐分指数变量组的BNN模型效果最佳,R2为0.651,RMSE为1.947;综合对比下,BNN模型的预测效果最好,可用于研究区土壤盐渍化预测。(2)银川平原主要是以非盐渍化和轻度盐渍化为主,0～20 cm土壤重度盐渍化及盐土共占总面积的11.59%,20～40 cm土壤重度盐渍化及盐土共占总面积的7.04%,20～40 cm土壤盐渍化程度较0～20 cm土壤盐渍化轻。 相似文献
18.
Soil moisture is an important parameter for agriculture, meteorological, and hydrological studies. This paper focuses on soil-moisture estimation methodology based on the multi-angle high-and low-incidence-angle mode RADARSAT-2 data obtained over bare agricultural fields in an arid area. Backscattering of the high-and low-incidence angles is simulated by using AIEM(advanced integral equation model), with the surface-roughness estimation model built based on the simulated data. Combining the surface-roughness estimation model with the backscattering model of the low-incidence-angle mode, a soil-moisture estimation method is put forward. First, the natural logarithm(ln) of soil moisture was obtained and then the soil moisture calculated. Soil moisture of the study area in Dunhuang, Gansu Province, was obtained based on this method; a good agreement was observed between the estimated and measured soil moisture. The coefficient of determination was 0.85, and the estimation precision reached 4.02% in root mean square error(RMSE). The results illustrate the high potential of the approach developed and RADARSAT-2 data to monitor soil moisture. 相似文献
19.
L. V. PEREZ-ARRIBAS F. NAVARRO-VILLOSLADA M. E. LEON-GONZALEA L. M. POLO-DIEZ Departamento de Quimica Analiti Facultad de Ciencias Quimicas Universidad Complutense E- Madri Spain 《地理学报(英文版)》1993,(4)
The usefulness of the Kalman filter as an algorithm for calibration in a real system is shown. Results arecompared with classical least squares and pure component calibration. The prediction of four prioritypollutant chlorophenols in binary, ternary and quaternary mixtures was also carried out by Kalmanfiltering. The condition number, standard deviation and prediction error have been employed to choosethe most suitable wavelength range. Comparison of the standard error of prediction in the validation setshows significant differences between the evaluated chlorophenols, the best results being obtained withKalman multivariate calibration. 相似文献
20.
混合像元的存在不仅影响了基于高光谱影像的地物识别和分类精度,而且已成为遥感科学向定量化发展的主要障碍。本文以扎龙湿地为试验区,以环境一号卫星采集的高光谱影像为数据源,分别采用传统的全约束最小二乘光谱解混算法(fully constrained least squares spectral unmixing algorithm, FCLS)与基于稀疏约束最小二乘光谱解混算法(sparse constrained least squares spectral unmixing algorithm, SUFCLS)实现了试验区湿地的精细分类,并对两种分类结果的表现及其分类精度进行了对比分析。研究结果表明：SUFCLS算法能够自适应的从光谱库中选择场景中所占比例最高的一组端元,并将此端元的组合应用于传统的全约束最小二乘光谱解混中实现不同湿地类型丰度的提取,该算法充分考虑了端元的空间异质性,弥补了FCLS算法在端元选取过程中的不足。精度验证结果表明与FCLS算法相比,SUFCLS算法分类结果的均方根误差更小,丰度的相关系数更高,因此该方法对于提高湿地解混精度以及实现湿地精细化分类具有重要意义。 相似文献