A univariate model for long-term streamflow forecasting

This paper, the first in a series of two, employs the principle of maximum entropy (POME) via maximum entropy spectral analysis (MESA) to develop a univariate model for long-term streamflow forecasting. Three cases of streamflow forecasting are investigated: forward forecasting, backward forecasting (or reconstruction) and intermittent forecasting (or filling in missing records). Application of the model is discussed in the second paper.     

REVIEWS OF GEOGRAPHIC SOFTWARE

Geographic Software Reviewed in this article: LIFE TABLES and the LESLIE MATRIX. CONDUIT MENTMAP2 . Lawrence W. Carstensen Jr . PC-MATLAB , Version 1.0. The Math Works, Inc. QUICKMAP , version 1.0. Sammamish Data Systems, Inc. SYSTAT: THE SYSTEM FOR STATISTICS , Version 2.1, Systat Inc. USA DISPLAY . Instant Tecall     

汉江流域极端水文事件时空分布特征

利用1960-2012年汉江流域15个气象站点的日降雨资料和3个水文站同时期日径流资料,分析了9个极端降雨指数的空间分布规律,运用广义极值分布（GEV）、Gamma分布两种极值统计模型对各站点的最大1 d降雨、最大3 d降雨极值样本进行拟合,遴选描述降雨极值分布规律最优概率模型,进而推算给定重现期下的降雨设计值,并分析其空间分布规律;选用Gumbel、Clayton和Frank这3种Copula函数建立降雨-洪量极值联合分布模型,优选最合适的Copula函数,由此计算给定重现期下的洪量设计值。结果表明：GEV分布模型能更好地模拟降雨极值序列,不同重现期下的降雨极值在空间上均呈西低东高的特征;3种Copula函数中,Frank Copula函数能更好地拟合降雨-洪量相关关系,由此推求的洪量设计值大于单变量拟合设计值。     

单变量特征选择的苏北地区主要农作物遥感识别

遥感识别多源特征综合和特征优选是提高遥感影像分类精度的关键技术。农作物遥感识别中,识别特征的相对单一和数量过多均会导致作物识别精度不理想。随机森林(random forests)采用分类与回归树(CART)算法来生成分类树,结合了bagging和随机选择特征变量的优点,是一种有效的分类方法。单变量特征选择(univariate feature selection)能够对每一个待分类的特征进行测试,衡量该特征和响应变量之间的关系,根据得分舍弃不好的特征,优选得到的特征用于分类。本文基于随机森林和单变量特征选择,利用多时相光谱信息、植被指数信息、纹理信息及波段差值信息,设计多组分类实验方案,对江苏省泗洪县的高分一号(GF-1)和环境一号(HJ-1A)影像进行分类研究,旨在选择最佳的分类方案对实验区主要农作物进行识别和提取。实验结果表明:(1)多源信息综合的农作物分类精度明显高于单一的原始光谱特征分类,说明不同类型特征的引入能改善分类效果;(2)基于单变量特征选择算法的优选特征分类效果最佳,总体精度97.07%,Kappa系数0.96,表明了特征优选在降低维度的同时,也保证了较高的分类精度。随机森林和单变量特征选择结合的方法可以提高遥感影像的分类精度,为农作物的识别和提取研究提供了有效的方法。     

空间直角坐标至大地坐标的直接严密变换

基于一元三次方程的求解，给出了空间直角坐标向大地坐标直接转换的严密计算公式，并用算例说明了其正确性。本文提出的方法完全可以替代现有的以数值迭代计算为基础的转换方法。     

Modeling tropical river runoff: A time dependent approach

Forecasting of rainfall and subsequent river runoff is important for many operational problems and applications related to hydrology. Modeling river runoff often requires rigorous mathematical analysis of vast historical data to arrive at reasonable conclusions. In this paper we have applied the stochastic method to characterize and predict river runoffofthe perennial Kulfo River in southem Ethiopia. The time series analysis based auto regressive integrated moving average （ARIMA） approach is applied to mean monthly runoff data with 10 and 20 years spans. The varying length of the input runoff data is shown to influence the forecasting efficiency of the stochastic process. Preprocessing of the runoff time series data indicated that the data do not follow a seasonal pattern. Our forecasts were made using parsimonious non seasonal ARIMA models and the results were compared to actual 10-year and 20-year mean monthly runoff data of the Kulfo River. Our results indicate that river runoff forecasts based upon the 10-year data are more accurate and efficient than the model based on the 20-year time series.     

基于交替变量局部梯度法对土质边坡的三维稳定性分析

临界滑动面的获得是土质边坡稳定性分析中一个必要的过程,然而现有分析方法难以保证该临界滑面的准确性,而且部分分析方法在计算过程中会陷入局部最小值陷阱。基于单变量方法和最速梯度法,提出了交替变量局部梯度法,该方法能够突破最优化方法应用在边坡稳定性分析上的瓶颈。首先,通过斯宾塞极限平衡方法和网格搜索法（或者其他确定临界滑面的方法）,获得边坡初始椭球状临界滑面;其次,在该滑面上布置若干节点作为变量,目标函数为安全系数Fs关于空间节点坐标Zi的方程,为了使目标函数快速降低,沿负梯度方向循环优化每个节点,当满足一定精度要求时,即可获得三维土质边坡非圆弧状临界滑动面;最后,通过算例证明该方法可行,计算结果可靠。     

农田土壤水分和盐分空间分布的指示克立格分析评价

运用非参数地质统计学的多元指示克立格法,结合单元指示克立格法,对黄河河套灌区55hm2的盐渍化土地上两个比较关键时期的土壤水分和盐分进行空间分布的分析,给出了同一时期土壤水盐和不同时期土壤盐分满足一定标准的综合概率图,并借助该案例详细介绍了多元指示克立格的原理、步骤及应用范围。指示克立格方法为不同尺度(从农田到区域)上水土资源质量的评价提供了新的方法,能为水土资源的现代决策管理提供指导。     

空间直角坐标至大地坐标的直接严密变换

基于一元三次方程的求解,给出了空间直角坐标向大地坐标直接转换的严密计算公式,并用算例说明了其正确性。本文提出的方法完全可以替代现有的以数值迭代计算为基础的转换方法。     

Application of the discordant outlier detection and separation system in the geosciences

Basically, two main types of statistical methods – robust and outlier-based – are available for handling experimental data; we document here the application of the outlier-based method. Due to the unavailability of a suitable software system for statistically correct application of the outlier-based method, a new computer program, DODESSYS (Discordant Outlier DEtection and Separation SYStem), was written for the application of 33 discordancy test variants to experimental data, constituting contaminated or uncontaminated normal statistical samples. We illustrate the application of the discordant outlier-based scheme by five specific examples; three include univariate data for which this procedure was specifically designed and two are for bivariate data for which this methodology can be easily adopted. We thus report new statistical information on two reference materials (granite G-2 and sediment IAEA-417), bryozoan species from eastern Oman, a new improved Na/K geothermometric equation, and a more significant correlation with water depth of the abundance of meiofauna from the Gulf of Mexico. Recently, two sets of multi-dimensional discrimination diagrams for basic as well as acid rocks have been proposed from statistically correct methodology of natural logarithm-transformation of element ratios; the diagrams also require that these ratios should be normally distributed. We present numerous examples of application of these new diagrams for inferring tectonic setting of Archaean to Recent rocks, both before and after testing the datasets for discordant outliers. We recommend that outlying observations should always be evaluated for their discordancy.