共查询到20条相似文献，搜索用时 160 毫秒
1.
Tesfahun Berhane Nurilign Shibabaw Aemiro Shibabaw Molalign Adam Abera A.Muhamed 《资源与生态学报(英文版)》2018,(3)
埃塞俄比亚咖啡价格波动很大,因此对国家经济发展的影响不容小视,对咖啡价格进行预测具有理论和实践意义。为了分析咖啡价格波动,我们采用来自埃塞俄比亚商品交易所(ECX)记录的2008年6月25日至2017年1月5日期间咖啡日收盘价数据。在这里,咖啡价格的性质是非平稳的,我们在单个线性状态空间模型上应用卡尔曼滤波算法来预测咖啡价格的最优值,主要通过使用均方根误差(RMSE)来评估用于预测咖啡价格的算法的性能。基于线性状态空间模型和卡尔曼滤波算法,均方根误差(RMSE)为0.000016375,说明该算法性能良好,研究结果可靠。 相似文献
2.
本文提出了一个基于温度的导数来计算温度指数的日平均温度随机模型,该模型提出了一个季节性均值及其波动率的计算方法,使用均值回归的Ornstein-Uhlenbeck过程来刻画日平均温度的变化。本文还采用连续的三阶自回归过程来模拟去除趋势和季节性影响后的温度演变过程,模型的模拟结果与从埃塞俄比亚国家气象厅获得的2005年1月1日至2015年12月31日11年间埃塞俄比亚Bahir Dar记录的数据非常吻合。验证后的近似公式很容易根据热日和冷日(heating degree days (HDD) and cooling degree days (CDD))等典型温度指数推导期货价格,也给出了数值例子来说明该方法的准确性。结果表明,本文提出的模型比其他模型能更好地预测CDD指数。 相似文献
3.
由于二氧化碳的大量排放,导致生态环境急剧恶化,全球各国对碳排放量的关注度越来越高.推出碳金融衍生品、完善碳交易体系是促进碳减排的重要手段,而合理的碳金融衍生品定价是推出相关金融产品的基础。本文首次采用双侧伽马分布拟合碳配额收益率序列,得到碳配额价格的波动率并对期权定价模型进行优化,最终求得了碳期权价格。结果表明：碳配额收益率序列近似服从双侧伽马分布,而且此模型用于碳期权定价具有合理性。随后,综合考虑连涨、连跌收益率之间的关系及成交量对价格的影响,利用双侧伽马分布推导出价格涨跌条件概率的公式,进行了数值验证。因此,双侧伽马分布在碳交易中可用于期权定价和价格涨跌概率推断。 相似文献
4.
耕地保护关系到国家粮食安全和经济社会可持续发展,对生态环境保护具有重要作用,快速精准的获取耕地土壤盐分含量及空间分布信息是耕地保护的必然要求。以宁夏平罗县为研究区,利用Landsat 9 OLI和Sentinel-1遥感影像,提取光谱指数和雷达极化组合指数,基于变量投影重要性法与灰度关联法筛选特征变量,然后运用反向传播神经网络、支持向量机和随机森林3种机器学习算法构建模型,并用最佳模型反演耕地土壤含盐量空间分布情况。结果表明：(1)利用变量投影重要性法筛选变量建立的模型验证集决定系数(R2)大于灰度关联法筛选变量建立的模型。(2)利用随机森林算法,组合光谱指数和雷达极化组合指数协同反演模型效果最佳,建模集R2为0.791,均方根误差(RMSE)为1.016,R2较单一数据源模型分别提高0.065和0.085,RMSE分别降低0.147和0.189;验证集R2为0.780,RMSE为1.132,R2较单一数据源模型分别提高0.091和0.237,RMSE分别降低0.175和0.3... 相似文献
5.
基于Landsat-8遥感数据和PROSAIL辐射传输模型反演叶面积指数 总被引：2，自引：1，他引：1
《干旱区地理》2016,(5)
为了探讨Landsat 8 OIL数据在LAI大范围反演方面的应用潜力,使用Landsat 8 OIL影像,通过PROSAIL辐射传输模型,采用3种波段组合(Band2-7,Band2-5,Band3-5)建立了3个模拟冠层反射率-叶面积指数(LAI)查找表,用2种代价函数(Geman and Mc Clure代价函数,均方根误差代价函数)实现了对玉米、土豆、森林LAI的定量反演,并用LAI-2200测量数据作为相对真值对反演精度进行评价。结果表明:(1)使用Landsat 8数据,通过PROSAIL模型反演叶面积指数的精度是可以接受的,RMSE范围为在[0.892 4,1.205 0],R2范围为[0.721 3,0.873 3]。(2)Band5(近红外),Band4(红)Band3(绿)的波段组合反演效果在3种组合中精度最高,平均RMSE=0.993 1,R2=0.787 3。(3)Geman and Mc Clure代价函数比常用的均方根误差代价函数得到了更高的反演精度,平均RMSE=0.940 5,R2=0.817 5。(4)相对最优的反演策略是Band5,Band4,Band3的波段组合结合GM代价函数,RMSE=0.892 4,R2=0.873 3。(5)存在玉米土豆的反演值普遍低于测量值,而森林的反演值普遍高于测量值的问题。 相似文献
6.
基于主成分分析土壤水分扩散率单一参数模型的BP神经网络模型 总被引：1，自引：0，他引：1
应用水平土柱法测定了杨凌地区典型粘壤土的水分扩散率,利用土壤水分扩散率的单对数模型和双对数模型对其进行了拟合,建立了土壤水分扩散率单一参数模型,基于主成分分析建立了单一参数模型中参数B的BP神经网络模型。结果表明：利用主成分分析可将研究区域土壤容重、有机质含量、粘粒含量、粗粉粒含量和砂粒含量综合成3个主成分;基于主成分分析建立的BP神经网络模型拟合的单一参数模型参数[B]的均方根误差RMSE为0.308 2;将拟合得到的参数B代入单一参数模型中对土壤水分扩散率进行预测,除去其中较大值的预测结果偏低外,其余土壤水分扩散率预测结果都比较接近实测值,预测结果的均方根误差RMSE为0.257 8,可利用基于主成分分析建立的BP神经网络模型预测单一参数模型中的参数B。 相似文献
7.
《地理与地理信息科学》2017,(2)
气温(Ta)是描述陆地气候环境的一个重要参数,其异常变化直接影响人类的生存环境,因此如何高精度地估算气温成为当前研究的热点。MODIS数据因其分辨率较低不能提供精细的地表信息,为此,本文以更高分辨率的Landsat8影像为数据源,结合自动气象站的气温数据,耦合经纬度、归一化植被指数、归一化建筑指数和改进的归一化水体指数等多种因子,建立了多窗口线性回归模型(Multi-Window Linear Regression Model,MWLR)。最后以浙江北部为研究区,使用MWLR模型对该地区冬季气温进行了估算,模型预测的RMSE在1.458~1.551℃之间,R~2在0.835~0.842之间,当窗口大小为3×3时取最优精度(RMSE=1.458℃,R~2=0.835),优于一般的空间内插方法。研究结果验证了利用MWLR模型和Landsat8影像进行气温估算的有效性,并提供了一种基于遥感数据在局部地区开展高精度和高分辨率气温估算的模型。 相似文献
8.
9.
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
10.
蒸腾速率(Tr)是植物生理生态学研究中表征蒸腾耗水的常用指标,研究植物的蒸腾耗水有助于了解当地生态系统稳定性和水资源的可持续利用,但在遥感应用尤其在干旱区遥感应用中很少被使用。本文以古尔班通古特沙漠南缘的主要建群种多枝柽柳(Tamarix ramosissima)作为研究对象,应用高光谱指数法对其Tr日变化过程进行研究,寻找和确定最佳的Tr光谱指数。选择的6个光谱指数判定系数R2介于0.06~0.73,其中简单比值(SR)光谱指数有最高的判定系数(R2=0.73)、较低的均方根误差(RMSE=0.24)和较为简单的形式,光谱范围处于近红外波段(1 645~1 655nm)/(1 775~1 785nm)。SR作为Tr最佳光谱指数,对植被水分关系变化敏感,能够较好地记录和监测Tr日变化过程,有益于揭示光谱指数物理和生理机制。 相似文献
11.
Tesfahun Berhane Nurilign Shibabaw Aemiro Shibabaw Molalign Adam Abera A. Muhamed 《资源与生态学报(英文版)》2018,9(3):302-305
Ethiopian coffee price is highly fluctuated and has significant effect on the economy of the country. Conducting a research on forecasting coffee price has theoretical and practical importance.This study aims at forecasting the coffee price in Ethiopia. We used daily closed price data of Ethiopian coffee recorded in the period 25 June 2008 to 5 January 2017 obtained from Ethiopia commodity exchange (ECX) market to analyse coffee prices fluctuation. Here, the nature of coffee price is non-stationary and we apply the Kalman filtering algorithm on a single linear state space model to estimate and forecast an optimal value of coffee price. The performance of the algorithm for estimating and forecasting the coffee price is evaluated by using root mean square error (RMSE). Based on the linear state space model and the Kalman filtering algorithm, the root mean square error (RMSE) is 0.000016375, which is small enough, and it indicates that the algorithm performs well. 相似文献
12.
In this paper we present a stochastic model for daily average temperature to calculate the temperature indices upon which temperature-based derivatives are written. We propose a seasonal mean and volatility model that describes the daily average temperature behavior using the mean-reverting Ornstein-Uhlenbeck process. We also use higher order continuous-time autoregressive process with lag 3 for modeling the time evolution of the temperatures after removing trend and seasonality. Our model is fitted to 11 years of data recorded, in the period 1 January 2005 to 31 December 2015, Bahir Dar, Ethiopia, obtained from Ethiopia National Meteorological Services Agency. The analytical approximation formulas are used to price heating degree days (HDD) and cooling degree days (CDD) futures. The suggested model is analytically tractable for derivation of explicit prices for CDD and HDD futures and option. The price of the CDD future is calculated, using analytical approximation formulas. Numerical examples are presented to indicate the accuracy of the method. The results show that our model performs better to predict CDD indices. 相似文献
13.
Modeling Short-Term Spatial and Temporal Variability of Groundwater Level Using Geostatistics and GIS 总被引：1，自引：0，他引：1
Deepesh Machiwal Amit Mishra Madan K. Jha Arun Sharma S. S. Sisodia 《Natural Resources Research》2012,21(1):117-136
Continuous depletion of groundwater levels from deliberate and uncontrolled exploitation of groundwater resources lead to
the severe problems in arid and semi-arid hard-rock regions of the world. Geostatistics and geographic information system
(GIS) have been proved as successful tools for efficient planning and management of the groundwater resources. The present
study demonstrated applicability of geostatistics and GIS to understand spatial and temporal behavior of groundwater levels
in a semi-arid hard-rock aquifer of Western India. Monthly groundwater levels of 50 sites in the study area for 36-month period
(May 2006 to June 2009; excluding 3 months) were analyzed to find spatial autocorrelation and variances in the groundwater
levels. Experimental variogram of the observed groundwater levels was computed at 750-m lag distance interval and the four
most-widely used geostatistical models were fitted to the experimental variogram. The best-fit geostatistical model was selected
by using two goodness-of-fit criteria, i.e., root mean square error (RMSE) and correlation coefficient (r). Then spatial maps of the groundwater levels were prepared through kriging technique by means of the best-fit geostatistical
model. Results of two spatial statistics (Geary’s C and Moran’s I) indicated a strong positive autocorrelation in the groundwater levels within 3-km lag distance. It is emphasized that the
spatial statistics are promising tools for geostatistical modeling, which help choose appropriate values of model parameters.
Nugget-sill ratio (<0.25) revealed that the groundwater levels have strong spatial dependence in the area. The statistical
indicators (RMSE and r) suggested that any of the three geostatistical models, i.e., spherical, circular, and exponential, can be selected as the
best-fit model for reliable and accurate spatial interpolation. However, exponential model is used as the best-fit model in
the present study. Selection of the exponential model as the best-fit was further supported by very high values of coefficient
of determination (r
2 ranging from 0.927 to 0.994). Spatial distribution maps of groundwater levels indicated that the groundwater levels are strongly
affected by surface topography and the presence of surface water bodies in the study area. Temporal pattern of the groundwater
levels is mainly controlled by the rainy-season recharge and amount of groundwater extraction. Furthermore, it was found that
the kriging technique is helpful in identifying critical locations over the study area where water saving and groundwater
augmentation techniques need to be implemented to protect depleting groundwater resources. 相似文献
14.
Bo Huang Li Zhang Bo Wu 《International journal of geographical information science》2013,27(3):379-398
Understanding the complexity of urban expansion requires an analysis of the factors influencing the spatial and temporal processes of rural–urban land conversion. This study aims at building a statistical land conversion model to assist in understanding land use change patterns. Specifically, GIS coupled with a logistic regression model and exponential smoothing techniques is used for exploring the effects of various factors on land use change. These factors include population density, slope, proximity to roads, and surrounding land use, and their influence on land use change is studied for generating a predictive model. Methods to reduce spatial autocorrelation in a logistic regression framework are also discussed. Primarily, an optimal sampling scheme that can eliminate spatial autocorrelation while maintaining adequate samples to allow the model to achieve the comparable accuracy as the spatial autoregressive model is developed. Since many of the previous studies on modeling the spatial complexity of urban growth ignored temporal complexity, a modified exponential smoothing technique is employed to produce a smoothed model from a series of bi‐temporal models obtained from different time periods. The proposed model is validated using the multi‐temporal land use data in New Castle County, DE, USA. It is demonstrated that our approach provides an effective option for multi‐temporal land use change modeling and the modeling results help interpret the land use change patterns. 相似文献
15.
首先,从理论角度探讨土地发展权内在机理,分析现行征地补偿价格的不足,提出了征地补偿价格应为集体农用土地价格与土地发展权价格之和,并以此构建基于土地发展权的征地补偿定价模型;其次,以周口市为例,应用构建的定价模型及农户问卷调查分析方法,测算了各县(区)基于土地发展权的征地补偿价格,并对测算结果和现行征地补偿价格进行了对比分析;最后,提出应从法律上确保农民土地发展权的权益,并在今后征地工作中逐步改革现行征地补偿制度,构建公平合理的征地补偿分配机制。 相似文献
16.
黑土区土壤有机质和全氮含量遥感反演研究 总被引：1，自引：0，他引：1
以东北典型黑土区耕地为研究区,以Sentinel-2A（全球环境与安全监测计划的第二颗卫星,于2015年6月23日发射）影像作为数据源,构建光谱指数,分别采用多元逐步线性回归（Multiple Stepwise Linear Regression, MSLR）和随机森林（Random Forest, RF）算法建立土壤有机质（SOM）和土壤全氮（STN）预测模型,并采用十折交叉验证方法评估模型的性能。研究对比分析了不同气候、土壤类型和地形下土壤有机质和全氮的空间分布差异。研究表明：① 海伦示范区的SOM和STN含量最高,其年均温最低,高程最高,年降水量多,SOM含量升高,其年均温最低,年降水量多,STN含量升高;② 与基于多元逐步线性回归算法建立的SOM和STN预测模型相比,随机森林算法建立的SOM和STN预测模型,有着更高的精度和稳定性;③ 运用RF算法建立的SOM反演模型的R2为0.96,均方根误差为5.49 g/kg,STN反演模型的R2为0.95,均方根误差为0.27 g/kg; ④ 不同示范区统一建立SOM和STN预测模型,有助于提高预测精度,实现跨区域建模与制图。 相似文献
17.
Evaluation and prediction of groundwater levels through specific model(s) helps in forecasting of groundwater resources. Among the different robust tools available, the Integrated Time Series (ITS) and Back-Propagation Artificial Neural Network (BPANN) models are commonly used to empirically forecast hydrological variables. Here, we discuss the modeling process and accuracy of these two methods in assessing their relative advantages and disadvantages based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and coefficient of efficiency (CE). The arid and semi-arid areas of western Jilin province of China were chosen as study area owing to the decline of groundwater levels during the past decade mainly due to overexploitation. The simulation results indicated that both ITS and BPANN are accurate in reproducing (fitting) the groundwater levels and the CE are 0.98 and 0.97, respectively. In the validation phase, the comparison of the prediction accuracy of the BPANN and ITS models indicated that the BPANN models is superior to the ITS in forecasting the groundwater levels time series in term of the RMSE, MAE and CE. 相似文献
18.
联合国粮农组织提出“全球重要农业文化遗产”(Globally Important Agricultural Heritage System,GIAHS)项目来保护重要农业遗产以及有关的景观、生物多样性、知识和文化保护体系。许多国家将这一项目作为维持传统农业系统活态性的政策工具。本研究以浙江兴化垛田全球重要农业文化遗产为案例地,研究这种自上而下政府主导的保护实践是否能够有效推动保护政策实施。研究采用社会调查,在遗产核心区以问卷形式来了解农民对农业系统的理解及其相应的保护意愿。研究发现垛田农业系统作为重要农业文化遗产,仍然面临着人口老化与农业人口外流问题。农民对“全球重要农业文化遗产”概念并不熟悉,很少将其与所依赖的农业系统相联系。他们认为前者是一种能够带来经济收入的头衔,而后者具有值得保护的非经济价值。由于农民认为GIAHS能否带来经济结果存在不确定性,因此认为政府在保护中应当承担主要责任。所以,当GIAHS无法促进农业系统地非经济价值向经济价值转化时,它作为一种保护工具尚未有效地鼓励农民积极参与保护。基于这一研究结果,我们建议GIAHS概念应当与农民认知相匹配,从而为农民提供保护动力。 相似文献
19.
Emily Benton Hite David Barton Bray Elvira Duran Armando Rincón-Gutiérrez 《社会与自然资源》2017,30(5):613-626
Studies of coffee agroecosystems have focused on their role in providing habitat for biodiversity across a range of management intensities. These studies have not taken into account the temporal and spatial transformations in coffee landscapes and their impacts on structural heterogeneity and biodiversity, nor systematically linked these transformations to farmer management responses to price and policy shocks. We utilize a coupled natural–human system framework to examine the historical transformations of the coffee landscape in a matrix of community-protected forests in a coffee-growing community in Oaxaca, Mexico, and study how those transformations impact tree biodiversity across a range of management options, including formerly certified organic and conventional coffee, abandonment, and conversion. The coffee landscape has historically transitioned from forests and fields (1950s–1960s) to one dominated by coffee (1970s–1980s) to a richly mosaic and biodiverse landscape (1990–2010) resulting from 43% recent abandonment and conversion of coffee back to forest and fields. 相似文献
20.
SRTM DEM高程精度评价 总被引：6，自引：1，他引：5
为了全面认识SRTM DEM数据精度特征并完善SRTM DEM数据精度评定方法,该文以我国1∶5万比例尺DEM为参考数据,以具有多种地貌类型的陕西省为实验样区,利用高程中误差模型及空间插值方法对SRTMDEM进行高程精度分析。结果表明:陕西省的SRTM DEM高程中误差在3.5~60.7 m,呈现出较为显著的空间分异特征;并且高程中误差与实验样区平均坡度有较强的指数相关性,拟合的指数函数具有较高的模拟精度。 相似文献