矢量数据的一种分层动态组织方法

提出矢量数据分层组织的一种动态结构，在该结构中，单点、单字符或符号作为基元，以属性串组织基元，并分层叠置。采用区别于链式结构的动态通用串结构及Ｃ语言予以实现。     

地下水资源系统多目标管理模型与模糊带权二次规划方法

本文以济南地下水资源系统保泉供水管理课题为例,分析研究地下水资源系统多目标管理问题。首先建立起地下水资源系统多目标管理模型;然后利用模糊数学理论和权重概念,提出一种求解多目标决策问题的模糊带权二次规划方法,此方法可以同时解决互为竞争、互为矛盾的各目标之间的不可公度性和主次性问题。实际计算表明,所建立的模型和提出的方法具有较强的实用性和通用性。     

Monthly suspended sediment load prediction using artificial intelligence: testing of a new random subspace method

ABSTRACT

The predictive capability of a new artificial intelligence method, random subspace (RS), for the prediction of suspended sediment load in rivers was compared with commonly used methods: random forest (RF) and two support vector machine (SVM) models using a radial basis function kernel (SVM-RBF) and a normalized polynomial kernel (SVM-NPK). Using river discharge, rainfall and river stage data from the Haraz River, Iran, the results revealed: (a) the RS model provided a superior predictive accuracy (NSE = 0.83) to SVM-RBF (NSE = 0.80), SVM-NPK (NSE = 0.78) and RF (NSE = 0.68), corresponding to very good, good, satisfactory and unsatisfactory accuracies in load prediction; (b) the RBF kernel outperformed the NPK kernel; (c) the predictive capability was most sensitive to gamma and epsilon in SVM models, maximum depth of a tree and the number of features in RF models, classifier type, number of trees and subspace size in RS models; and (d) suspended sediment loads were most closely correlated with river discharge (PCC = 0.76). Overall, the results show that RS models have great potential in data poor watersheds, such as that studied here, to produce strong predictions of suspended load based on monthly records of river discharge, rainfall depth and river stage alone.     

A minimalistic approach for evapotranspiration estimation using the Prophet model

ABSTRACT

This study aimed to evaluate the potential of the recently introduced Prophet model for estimating reference evapotranspiration (ETo). A comparative study was conducted for benchmarking the model results with support vector regression (SVR) and temperature-based empirical models (Thornthwaite and Hargreaves) in southern Japan. The performance of the Prophet, SVR and temperature-based empirical models was evaluated by Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R²). The results indicate that temperature-based Prophet and SVR models have greater accuracy than the empirical models. The Prophet model with sole input of relative humidity, sunshine hours or windspeed showed acceptable accuracy (NSE > 0.80; R² > 0.80), while SVR models with similar inputs showed greater errors. Accuracy improved with increasing number of input parameters, giving excellent performance (NSE > 0.95; R² > 0.95) with all input parameters. Hence, the Prophet model is a new promising approach for modelling ETo with limited input variables.     

Improvement of spatial interpolation accuracy of daily maximum air temperature in urban areas using a stacking ensemble technique

ABSTRACT

The reliable and robust monitoring of air temperature distribution is essential for urban thermal environmental analysis. In this study, a stacking ensemble model consisting of multi-linear regression (MLR), support vector regression (SVR), and random forest (RF) optimized by the SVR is proposed to interpolate the daily maximum air temperature (T_max) during summertime in a mega urban area. A total of 10 geographic variables, including the clear-sky averaged land surface temperature and the normalized difference vegetation index, were used as input variables. The stacking model was compared to Cokriging, three individual data-driven methods, and a simple average ensemble model, all through leave-one-station-out cross validation. The stacking model showed the best performance by improving the generalizability of the individual models and mitigating the sensitivity to the extreme daily T_max. This study demonstrates that the stacking ensemble method can improve the accuracy of spatial interpolation of environmental variables in various research fields.     

顾及地理矢量场空间变化特征的多分辨率纹理可视化方法

网络环境下海量、高维、动态、多尺度地理空间矢量场数据的在线高效可视化,一直是大规模密集矢量场数据集成与综合应用分析亟须解决的关键问题。地理矢量场数据蕴含大量非规则、多尺度、时空异质的变化特征与结构,传统矢量场纹理可视化方法,受全局统一采样密度的限制,存在典型的稠密遮挡、特征模糊的问题。针对上述问题,本文提出了一种顾及地理矢量场空间变化特征的多分辨率纹理可视化方法,引入复合信息熵定量显式描述矢量场的空间变化,以此驱动图像空间内多频噪声纹理生成、矢量场多分辨率纹理的构建与增强表达,有效克服传统纹理可视化方法的技术瓶颈。最后采用风场数据进行试验,验证了本文方法的可靠性与有效性。     

Impulsive time-free transfers between halo orbits

A methodology is developed to design optimal time-free impulsive transfers between three-dimensional halo orbits in the vicinity of the interior L ₁ libration point of the Sun-Earth/Moon barycenter system. The transfer trajectories are optimal in the sense that the total characteristic velocity required to implement the transfer exhibits a local minimum. Criteria are established whereby the implementation of a coast in the initial orbit, a coast in the final orbit, or dual coasts accomplishes a reduction in fuel expenditure. The optimality of a reference two-impulse transfer can be determined by examining the slope at the endpoints of a plot of the magnitude of the primer vector on the reference trajectory. If the initial and final slopes of the primer magnitude are zero, the transfer trajectory is optimal; otherwise, the execution of coasts is warranted. The optimal time of flight on the time-free transfer, and consequently, the departure and arrival locations on the halo orbits are determined by the unconstrained minimization of a function of two variables using a multivariable search technique. Results indicate that the cost can be substantially diminished by the allowance for coasts in the initial and final libration-point orbits.An earlier version was presented as Paper AIAA 92-4580 at the AIAA/AAS Astrodynamics Conference, Hilton Head Island, SC, U.S.A., August 10–12, 1992.     

基于? -SVR算法的隧道围岩位移演化规律预测

针对目前广泛应用的灰色理论、遗传算法(GA)和人工神经网络(ANN)等方法预测隧道稳定性的缺陷，提出应用稳健性能较好的? -SVR（support vector machine）算法对非对称连拱隧道围岩位移演化规律进行预测研究。应用加速混合遗传算法搜索? -SVR最优参数，以提高? -SVR的预测能力。将预测结果与灰色理论、BP神经网络预测结果进行比较，显示? -SVR算法学习和预测精度高。     

基于直方图的遥感图像相似性检索方法比较

相似性度量是用于研究多源数据之间相似程度的，是对空间数据进行模式识别的基础。通过单波段遥感图像的检索对两组直方图相似性检索方法进行了实验研究，即基于特征向量的相似性度量和基于概率的相似性度量。实验中发现第一组相似度量中有两种以往较少用于遥感图像检索的方法表现出色，它们分别是，统计距离和相似夹角余弦度量。第二组实验中，针对其中包含较明显的目标物体且背景较为单一的遥感图像（其直方图可看作混合高斯分布），在类别可分离判据的基础上，根据K-近邻法则提出了一种计算该类图像之间相似值的方法。实验结果表明基于K-近邻法则的计算方法行之有效。所得出的结论将对多源数据分析中相似性度量的理解与选择有积极意义。