共查询到20条相似文献，搜索用时 15 毫秒
1.
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
2.
基于模式优选的21世纪中国气候变化情景集合预估 总被引：1，自引：1，他引：0
未来气候变化情景预估是制定气候变化应对和适应策略的科学基础。本文利用参与耦合模式比较计划第五阶段（CMIP5）的30个气候模式的模拟数据,通过评估各模式对历史气候变化的模拟能力,筛选出模拟区域气候变化的最优模式组合,进而建立偏最小二乘回归（PLS）集合预估模型,据此利用最优模式模拟结果预估区域温度和降水变化情景。通过与历史数据的对比,研究发现本文基于最优模式建立的PLS集合预估模型不仅优于传统的多模式集合平均,而且也优于利用全部模式建立的PLS集合预估模型,体现了模式优选过程的重要性。本文基于优选模式的PLS集合预估模型预估结果表明：① 21世纪各区域温度将持续上升,且冬半年升温速率总体大于夏半年,北方地区升温速率总体高于南方地区;RCP 4.5排放情景下温度上升先快后慢,转折点出现在21世纪中期,RCP 8.5排放情景下,呈持续增加趋势,至21世纪末的升温幅度约为RCP 4.5情景的2倍。② 21世纪各区降水变化均呈显著增加趋势,并表现出高排放情景大于低排放情景,少雨区大于多雨区的特征,但是降水增加过程伴有明显的年代际波动。对比发现,传统的等权重集合平均全部模式（EMC）方法预估的中国夏季变暖速率高于冬季,且降水基本呈线性增加,有悖于全球变暖的基本特征及中国降水具有鲜明的年代际变化特征的基本认识。因而,本文预估的温度和降水变化特征均更符合中国气候变化的基本规律。 相似文献
3.
High resolution climate simulations over the Arctic 总被引：1，自引：0，他引：1
Annette Rinke Klaus Dethloff Arne Spekat Wolfgang Enke Jens Hesselbjerg Christensen 《Polar research》1999,18(2):143-150
The regional atmospheric climate model HIRHAM has been applied to the Arctic. Simulations for the whole year 1990 and for an ensemble of winter months (January of 1985-1995) have been performed. The comparison of the simulations with observational data analyses shows that the general spatial patterns are in good agreement with the data, in both the vertical structure and the annual cycle. For an additional validation of the model results, a multivariate classification of large-scale circulation patterns has been applied to the January ensemble model simulations. 相似文献
4.
Yin Xin Liu Quansheng Pan Yucong Huang Xing Wu Jian Wang Xinyu 《Natural Resources Research》2021,30(2):1795-1815
Rockburst is a common dynamic geological hazard, severely restricting the development and utilization of underground space and resources. As the depth of excavation and mining increases, rockburst tends to occur frequently. Hence, it is necessary to carry out a study on rockburst prediction. Due to the nonlinear relationship between rockburst and its influencing factors, artificial intelligence was introduced. However, the collected data were typically imbalanced. Single algorithms trained by such data have low recognition for minority classes. In order to handle the problem, this paper employed stacking technique of ensemble learning to establish rockburst prediction models. In total, 246 sets of data were collected. In the preprocessing stage, three data mining techniques including principal component analysis, local outlier factor and expectation maximization algorithm were used for dimension reduction, outlier detection and outlier substitution, respectively. Then, the pre-processed data were split into a training set (75%) and a test set (25%) with stratified sampling. Based on the four classical single intelligent algorithms, namely k-nearest neighbors (KNN), support vector machine (SVM), deep neural network (DNN) and recurrent neural network (RNN), four ensemble models (KNN–RNN, SVM–RNN, DNN–RNN and KNN–SVM–DNN–RNN) were built by stacking technique of ensemble learning. The prediction performance of eight models was evaluated, and the differences between single models and ensemble models were analyzed. Additionally, a sensitivity analysis was conducted, revealing the importance of input variables on the models. Finally, the impact of class imbalance on the prediction accuracy and fitting effect of models was quantitatively discussed. The results showed that stacking technique of ensemble learning provides a new and promising way for rockburst prediction, which exhibits unique advantages especially when using imbalanced data.
相似文献5.
Peng Kang Zeng Jie Armaghani Danial Jahed Hasanipanah Mahdi Chen Qiusong 《Natural Resources Research》2021,30(6):4657-4671
Natural Resources Research - An ensemble technique namely gradient boosted tree (GBTs) and several optimized neural network models were hybridized to predict peak particle velocity (PPV) caused by... 相似文献
6.
D. A. Burakov I. N. Gordeev A. V. Ignatov O. E. Petkun L. A. Putintsev A. A. Chekmarev 《Geography and Natural Resources》2016,37(2):158-164
We consider the various methods of constructing models intended to forecast the average water inflow, in the second quarter of the year, into two reservoirs on the Yenisei river. To solve modeling problems used a new computer technology implemented in the specialized “Stochastic Modeling” software package. Independent data were employed to verify the variants of the models for the formation of variability in quarterly inflow as generated based on different algorithms. A more sophisticated and robust model for forecasting the inflow was constructed as an ensemble of partial models. Based on aggregate results of modeling, we suggest the method of constructing a forecast of the average (for the second quarter) lateral inflow into the Krasnoyarsk reservoir and the inflow into the Sayano-Shushenskoe reservoir by use of observational data accumulated by Srednesibirskoe UGMS (Weather Control and Environmental Monitoring Service), based on an ensemble of partial models. It is established that such an operation reduces the probability of forecasting errors implying an arbitrary selection of models. We constructed forecasts of the aforementioned characteristics using real-time data for 2015. It is stated that the solution of the forecasting problem can be facilitated by using additional information. 相似文献
7.
Lin Wan Zhou Huang Xia Peng Ran Li 《International journal of geographical information science》2018,32(11):2225-2246
Geo-tagged travel photos on social networks often contain location data such as points of interest (POIs), and also users’ travel preferences. In this paper, we propose a hybrid ensemble learning method, BAyes-Knn, that predicts personalized tourist routes for travelers by mining their geographical preferences from these location-tagged data. Our method trains two types of base classifiers to jointly predict the next travel destination: (1) The K-nearest neighbor (KNN) classifier quantifies users’ location history, weather condition, temperature and seasonality and uses a feature-weighted distance model to predict a user’s personalized interests in an unvisited location. (2) A Bayes classifier introduces a smooth kernel function to estimate a-priori probabilities of features and then combines these probabilities to predict a user’s latent interests in a location. All the outcomes from these subclassifiers are merged into one final prediction result by using the Borda count voting method. We evaluated our method on geo-tagged Flickr photos and Beijing weather data collected from 1 January 2005 to 1 July 2016. The results demonstrated that our ensemble approach outperformed 12 other baseline models. In addition, the results showed that our framework has better prediction accuracy than do context-aware significant travel-sequence-patterns recommendations and frequent travel-sequence patterns. 相似文献
8.
风沙流中沙粒随机运动的数值模拟研究 总被引：15，自引：8，他引：7
通过对描述沙粒垂向运动速度脉动分量的随机微分方程的直接求解,获得了风沙流中沙粒运动的随机轨迹。结果表明,由于沙粒垂向脉动速度的影响,沙粒的轨迹与不考虑其垂向脉动速度的情形存在明显不同。在此基础上,通过对大量轨迹的统计计算,得到了沙粒浓度的分布规律。 相似文献
9.
利用CRU月降水资料首先对参与IPCC第五次评估报告（IPCC AR5）的10个CMIP5模式对1951-2005年中亚地区年降水气候平均态、年际变率以及线性趋势等特征参数的模拟能力进行了系统评估,并选取具有较好模拟性能模式的未来预估试验结果作多模式集合平均预估未来50 a（2011-2060年）中亚地区在不同代表性浓度路径下降水量各特征参数的空间分布特征,结果表明：多数模式能够模拟出中亚地区年降水气候平均态、年际变率以及线性趋势的空间分布特征,同时发现中亚地区年降水量在过去50 a整体以轻微增加为主,趋势不显著。根据定量评估结果,从10个模式中选取4个具有较好模拟性能的模式结果做集合平均,同时利用历史回报试验数据进行检验,发现集合平均的模拟结果无论在量级还是高、低值中心的位置和范围与CRU资料非常接近。未来预估结果表明4种排放情景下4模式集合平均的中亚年降水在未来50 a增加较为明显,尤其在中国新疆南部（由低值区转变为高值区）。总体来看,未来50 a中亚降水增加趋势随着RCPs的增加而增加,且降水增加显著的区域随着RCPs的增加而明显增大。 相似文献
10.
基于多源数据和集成学习的城市住宅地价分布模拟——以武汉市为例 总被引：2，自引：0，他引：2
精准刻画城市住宅地价分布特征,对于科学引导城市空间布局规划、有效实现城市精明增长等具有重要意义。而城市住宅地价与其潜在影响因素之间的复杂非线性关系,给地价分布精细模拟带来了挑战。论文旨在探索基于地理大数据和集成学习的城市住宅地价分布模拟方法体系,以满足快速、精准监测地价动态变化的需要。选取武汉市为典型区,以住宅用地交易样点、兴趣点(points of interest, POI)和夜间灯光影像为数据源,以500 m分辨率网格为估价单元,提取POI核密度和夜间灯光强度作为住宅地价预测变量,采用机器学习算法和bagging、stacking集成方法构建住宅地价预测模型,并对比分析其精度。研究发现：① 单个机器学习算法中,支持向量回归(support vector regression, SVR)预测精度最高,接下来依次是k最近邻算法(k-nearest neighbor algorithm, k-NN)、高斯过程回归(Gaussian process regression, GPR)和BP神经网络(back propagation neural networks, BP-NN);② 在提升单个算法预测精度方面,stacking方法的性能优于bagging方法,使用stacking集成SVR和k-NN的地价预测模型精度最高,其平均绝对百分误差仅为8.29%,拟合优度R2达0.814;③ 基于论文所构建模型生成的城市住宅地价分布图能有效表征价格圈层分布特征和局部奇异性。研究结果可为城市住宅地价评估提供新的思路和方法借鉴。 相似文献
11.
B. L. N. Kennett 《Geophysical Journal International》1978,55(2):373-391
Summary. A direct attempt is made to examine the influence of non-linearity, in the dependence of data on parameters, on model resolution. An extension of the Marquardt approach to the non-linear case leads to bounded domains in discrete parameter space for a specified level of data misfit. The simplest class of non-linear influences on these domains may be simulated by modified linear terms via perturbation theory. Asymmetric non-linear effects are considered using an ensemble of attempts at the solution of the inverse problem. 相似文献
12.
N. J. Lim S. A. Brandt S. Seipel 《International journal of geographical information science》2016,30(2):240-262
This study evaluates how users incorporate visualisation of flood uncertainty information in decision-making. An experiment was conducted where participants were given the task to decide building locations, taking into account homeowners’ preferences as well as dilemmas imposed by flood risks at the site. Two general types of visualisations for presenting uncertainties from ensemble modelling were evaluated: (1) uncertainty maps, which used aggregated ensemble results; and (2) performance bars showing all individual simulation outputs from the ensemble. Both were supplemented with either two-dimensional (2D) or three-dimensional (3D) contextual information, to give an overview of the area.
The results showed that the type of uncertainty visualisation was highly influential on users’ decisions, whereas the representation of the contextual information (2D or 3D) was not. Visualisation with performance bars was more intuitive and effective for the task performed than the uncertainty map. It clearly affected users’ decisions in avoiding certain-to-be-flooded areas. Patterns to which the distances were decided from the homeowners’ preferred positions and the uncertainties were similar, when the 2D and 3D map models were used side by side with the uncertainty map. On the other hand, contextual information affected the time to solve the task. With the 3D map, it took the participants longer time to decide the locations, compared with the other combinations using the 2D model.
Designing the visualisation so as to provide more detailed information made respondents avoid dangerous decisions. This has also led to less variation in their overall responses. 相似文献
13.
Mehmet Fatih Uluat Veysi İşler 《International journal of geographical information science》2016,30(6):1117-1136
Prefetching is a process in which the necessary portion of data is predicted and loaded into memory beforehand. The increasing usage of geographic data in different types of applications has motivated the development of different prefetching techniques. Each prefetching technique serves a specific type of application, such as two-dimensional geographic information systems or three-dimensional visualization, and each one is crafted for the corresponding navigation patterns. However, as the boundary between these application types blurs, these techniques become insufficient for hybrid applications (such as digital moving maps), which embody various capabilities and navigation patterns. Therefore, a set of techniques should be used in combination to handle different prefetching requirements. In this study, a priority-based tile prefetching approach is proposed, which enables the ensemble usage of various techniques at the same time. The proposed approach manages these techniques dynamically through a fuzzy-logic-based inference engine to increase prefetching performance and to adapt to various exhibited behaviours. This engine performs adaptive decisions about the advantages of each technique according to their individual accuracy and activity level using fuzzy logic to determine how each prefetching technique performs. The results obtained from the experiments showed that up to a 25% increase in prefetching performance is achieved with the proposed ensemble usage over individual usage. A generic model for prefetching techniques was also developed and used to describe the given approach. Finally, a cross-platform software framework with four different prefetching techniques was developed to let other users utilize the proposed approach. 相似文献
14.
Malcolm Sambridge 《Geophysical Journal International》1999,138(2):479-494
This paper presents a new derivative-free search method for finding models of acceptable data fit in a multidimensional parameter space. It falls into the same class of method as simulated annealing and genetic algorithms, which are commonly used for global optimization problems. The objective here is to find an ensemble of models that preferentially sample the good data-fitting regions of parameter space, rather than seeking a single optimal model. (A related paper deals with the quantitative appraisal of the ensemble.)
The new search algorithm makes use of the geometrical constructs known as Voronoi cells to derive the search in parameter space. These are nearest neighbour regions defined under a suitable distance norm. The algorithm is conceptually simple, requires just two 'tuning parameters', and makes use of only the rank of a data fit criterion rather than the numerical value. In this way all difficulties associated with the scaling of a data misfit function are avoided, and any combination of data fit criteria can be used. It is also shown how Voronoi cells can be used to enhance any existing direct search algorithm, by intermittently replacing the forward modelling calculations with nearest neighbour calculations.
The new direct search algorithm is illustrated with an application to a synthetic problem involving the inversion of receiver functions for crustal seismic structure. This is known to be a non-linear problem, where linearized inversion techniques suffer from a strong dependence on the starting solution. It is shown that the new algorithm produces a sophisticated type of 'self-adaptive' search behaviour, which to our knowledge has not been demonstrated in any previous technique of this kind. 相似文献
The new search algorithm makes use of the geometrical constructs known as Voronoi cells to derive the search in parameter space. These are nearest neighbour regions defined under a suitable distance norm. The algorithm is conceptually simple, requires just two 'tuning parameters', and makes use of only the rank of a data fit criterion rather than the numerical value. In this way all difficulties associated with the scaling of a data misfit function are avoided, and any combination of data fit criteria can be used. It is also shown how Voronoi cells can be used to enhance any existing direct search algorithm, by intermittently replacing the forward modelling calculations with nearest neighbour calculations.
The new direct search algorithm is illustrated with an application to a synthetic problem involving the inversion of receiver functions for crustal seismic structure. This is known to be a non-linear problem, where linearized inversion techniques suffer from a strong dependence on the starting solution. It is shown that the new algorithm produces a sophisticated type of 'self-adaptive' search behaviour, which to our knowledge has not been demonstrated in any previous technique of this kind. 相似文献
15.
《Basin Research》2018,30(4):730-745
We consider the problem of conditioning a geological process‐based computer simulation, which produces basin models by simulating transport and deposition of sediments, to data. Emphasising uncertainty quantification, we frame this as a Bayesian inverse problem, and propose to characterise the posterior probability distribution of the geological quantities of interest by using a variant of the ensemble Kalman filter, an estimation method which linearly and sequentially conditions realisations of the system state to data. A test case involving synthetic data is used to assess the performance of the proposed estimation method, and to compare it with similar approaches. We further apply the method to a more realistic test case, involving real well data from the Colville foreland basin, North Slope, Alaska. 相似文献
16.
The Florida State University (FSU) multimodel superensemble forecast is evaluated against several other operational weather models for the Southeast Asia region. The superensemble technique has demonstrated its exceptional skills in forecasting precipitation, motion and mass fields compared to either individual global operational or ensemble mean forecasts. The motion field investigation for the season of 2001 reveals that the superensemble forecasts are closer to the observed data compared to the other global member operational models through its low systematic errors at the 850 hPa level. The FSU multimodel superensemble forecasts exhibit the lowest root mean square errors (RSMEs), the highest correlation against the best observed data and the lowest systematic errors compared to the other operational model members. These forecasts have the potential to provide better daily weather predictions over the Southeast Asia region, particularly during the early northeast monsoon that often causes heavy rainfall in the equatorial part of the Southeast Asia region. 相似文献
17.
Yihan Zhang Xiaoping Liu Jigang Qiao 《International journal of geographical information science》2013,27(9):1612-1631
There are many different methods to calibrate cellular automata (CA) models for better simulation results of urban land-use changes. However, few studies have been reported on combination of parameter update and error control using local data in CA calibration procedures. This paper presents a self-modifying CA model (SM-CA) that uses the dual ensemble Kalman filter (dual EnKF), which enables the CA model to simultaneously update model parameters and simulation results by merging observation data (local data). We applied the proposed model to simulate urban land-use changes in a 13-year period (1993–2005) in Dongguan City, a rapidly urbanizing region in south China. Simulation results indicate that this model yields better simulation results than the conventional logistic-regression CA and decision-tree CA models. For example, the validation is carried out using cross-tabulation matrix. The simulation results of SM-CA have allocation disagreement of 10.18%, 19.64%, and 30.03% in 1997, 2001, and 2005, respectively, which are 2.12%, 2.47%, and 6% lower than conventional logistic-regression CA models. 相似文献
18.
多模式气候预估对华北冬小麦产量模拟的不确定性分析 总被引：1，自引：0，他引：1
基于CMIP5的多模式气候预估资料,应用集合方法,评估了未来中国华北地区冬小麦产量受气候变化影响的不确定性,并给出未来中国华北冬小麦增产或减产可能的概率。利用CMIP5的15个全球气候模式2006-2030年4种排放情景的54组逐日气候预估结果,运用CERES-Wheat模型模拟了未来华北地区冬小麦的产量。结果表明,气温的预估结果较好,降水量和太阳辐射的气候预估值的不确定性较大。河北、山东和河南的3个代表点小麦产量的模拟集合表明,未来冬小麦产量年际波动较大,以弱增产的概率为主,但是随气候变化的冬小麦产量的低产概率明显上升。最后本文还给出了2011-2030年间华北地区冬小麦产量不同等级的概率分布。 相似文献
19.
东北气候和生态过渡区近50年来降水和温度概率分布特征变化 总被引：18，自引：11，他引：7
文章从中国160个站的观测资料中选取位于东北气候和生态过渡区内9个测站的冬、夏季降水和温度资料,分析该地区近50年来冬夏季降水和温度的年际变化及其概率分布特征,结果表明,东北气候和生态过渡区的冬夏季降水和温度有明显的年代际变化特征,在不同的年代际变化阶段,降水和温度的总体概率分布特征差异较大,这种概率分布形式的差异与高温、干旱等极端天气气候事件的频繁发生具有密切关系。20世纪80年代以来降水处于平均值减小的总体分布中,温度则处于平均值增加的总体分布中,因此该地区冬季发生暖冬和少雨(雪)的机会增大,夏季出现严重干旱和高温的可能性增大。 相似文献
20.
RCP8.5气候变化情景下21世纪印度粮食单产变化的多模式集合模拟 总被引：1，自引：0，他引：1
基于跨部门影响模型比较计划（ISI-MIP）中20种气候模式与作物模型组合的模拟结果,预估了RCP 8.5排放情景下21世纪印度小麦和水稻单产变化。研究发现：① 多模式集合模拟结果基本再现了印度小麦和水稻单产的空间差异;同时,再现了小麦和水稻单产对温度和降水变化的响应特征：与温度呈负相关,与降水呈正相关。② RCP 8.5情景下,水稻和小麦生长季温度和降水均呈增加趋势,小麦生长季的温度、降水增加幅度大于水稻。空间上,温度增加幅度自北向南逐渐减小,降水增幅则逐渐增加,并且小麦种植区升温幅度大于非种植区,降水增幅则少于非种植区,水稻种植区升温幅度小于非种植区,降水增幅则多于非种植区。③ RCP 8.5情景下,小麦和水稻单产均呈下降趋势,21世纪后半叶尤为明显。小麦单产的下降速度明显大于水稻,其中21世纪前半叶小麦和水稻单产下降速度约分别为1.3%/10a （P < 0.001）和0.7%/10a （P < 0.05）,后半叶分别增至4.9%/10a （P < 0.001）和4.4%/10a （P < 0.001）。小麦和水稻单产变化存在明显的空间异质性,小麦单产的最大下降幅度出现在德干高原西南部,降幅约60%,水稻单产最大下降幅度出现在印度河平原北部,降幅约50%。这意味着未来气候变化情景下印度粮食供给将面临较大的挑战。 相似文献